The book teaches you the importance of love, meaning of life, meaning of death.Author’s Note
Before reading, I would like you to know that this isn’t written like any ordinary book. It’s more of a way to express my thoughts that come to me as the days go by. Actually my days aren’t very ordinary either. Not for an 18 year old girl that is. My name is Aisha. I was born with S.C.I.D (severe combined immune deficiency) and underwent a bone marrow transplant when I was 6 months old, in the UK. I now live in New Delhi, India, where I was born. I have developed Pulmonary Fibrosis, which is a hardening in the lungs. I can’t breathe therefore spend each moment connected to an oxygen tank, and use a wheelchair when leaving the house, as my heart cannot take it when I stand. Unfortunately, everyone has their problems and this is mine. I have felt isolated, and completely stuck. So I decided that its time to reach out. I wanted to share my thoughts with the world, I wanted to let peopleknow that they are not alone, and regardless of what the problem is, we all feel the same, and we are all fighting our own battles together. This book is about finding myself, letting go and expressing who I am, and I do hope that by the end of the book, you will have found a piece of yourself too.Dedication
Dear Tanya, I’d like this book to be in honor of you. You are one of the many reasons I live today. If it weren’t for you, I wouldn’t be writing this. We went through the same thing, but you left the world as a baby. You will live in our hearts forever. I want to live tomorrow for us too. I sometimes look up at the sky and smile, and feel you smiling down at me. I have never known you, but have always loved you my sweet sister. I’ll meet you in the skies my angel. Love, Aisha # My dearest darling, Rolo, I am just lost for words. I don’t know how to breathe without you, my baby. You were the light of my life, you were one of the main reasons I forced myself to wake up every morning. You were my strength. You are my everything. You always took my illness upon yourself. I just wish you didn’t take it this far. You left me so suddenly, but I have to remind myself that bad things happen for good reasons. You went to heaven at 8:30 am on 2 nd December 2014, the day after Tanya’s birthday. I like to believe that God gave you to her, for her birthday present. She probably needs you more than I do, my sweetheart. I know you are in good hands, and are probably licking her face nonstop right now, as you once did mine. I imagine that she knows by now that you always yawn if she itches your cheeks, just like I once did yours. You brought so much happiness into my life, you were the sassiest pug I had ever come across, and I just have to be grateful that I got to know andlove you. You were the magic in my heart and you always will be. I will never forget you, “Rolito the Burrito”. Tanya and I shared the same disease, and now we will share you too. You are her angel now. Look after each other. I will love you forever. Sweet dreams, my precious. Love, Aisha # Fairy dust in your eyes I could see no more They say the soul never dies What did yours leave me for My darling you have kept me alive You soothed the rocky roads before me I'm shattered now you're dead inside Burnt into thin air, this was our destiny. Until we meet again, one day our worlds will collide. Till then you always own a piece of my heart Just tell Him not to further break it apart.My Little Epiphanies
The fact that I’m sitting here, writing these words, is a miracle. I would not have been here on this Earth for more than a year, had destiny not changed its mind. # We’ve all grown up, so why are we still playing the game of hide and seek? # Would the world be so bad if we were all friends? # We all definitely have one thing in common, and that is death. # What I’m going through is actually better than what someone else is going through out there. But because we are so unaware and invested in our own lives, we can focus on nothing but the shit; therefore, we will always be extremely miserable human beings no matter what happens. # Life is a circle and we all think we are bang in the middle of it. # She loved him but he did not love her and the stars were so perfect that night, but then my darling, the darkness kicked in. # I hate myself for tripping into this beautiful thing everybody calls love.I need a whole new pair of shoes. I’m running from myself this time. # I need to get over this and move on. It’s making me too sad to enjoy my precious days on this planet. I can’t give him the power to take that away from me too. # I crave the littlest things, while I’ve lost the biggest.Why do so many emotions exist? # That’s everybody’s problem, we keep looking up to find answers. Now, let’s look within us.Something must be wrong when the music is blasting, but all you can hear is that deafening silence. # When I’m high, it’s like my soul is telling me a secret. # Death is a tricky business, and we are just the employees. # Are we living to die? Or are we dying to live? I want to do the latter. # It’s funny how we give each other blood in order to survive. If we are willing to do that, I don’t understand how there is so much hate in the world. # Don’t worry about what happened today, death is on its way I promise you. # You’re not my reality, because you are just the reverse. # Heaven is not a place. Heaven is our home. # What is the definition of love? # A knot at the pit of my belly; a blessing and a curse. A gut wrenching pain, as though I’ve been stabbed; only instead of blood, butterflies disperse. # Do you ever see a stranger and think that they look exactly like someone from your past… only 10 years older? Let’s live and love with no regrets. I can hear your heart beating one after the other, like dancing rain on a dark, silent night. My heart is tingling. Mama says, ‘Darling, if you have to go through shit, do it looking like a million bucks’. Are we all fake at some level? Shhh…miracles are happening. Maybe in 10 years, you will be looking from the other side of the road, and reminiscing about this time. My mother must worry with every bone in her body. It feels like I’m watching my world from above. Let’s wake up from this dream and turn it into a reality. Thoughts come into your head 100 times faster, increasing exponentially. We are the oldest we will ever be right now. My lungs feel like ropes that have been tangled and knotted together; churning around in the pit of my belly, while my heart aches and cries in pain. Only, it’s not the disease this time. Being depressed just means that I’m under repairs. If life is a stop in the station, I must admit I don’t want to get on the coming train. I know there are healthy people out there who feel as shitty as I do. But I don’t know what healthy is anymore; I’m stuck here, so how can I really know about anybody else? It has come to a point where I feel embarrassed of my dreams. In the end, overthinking is poison to the heart. How is my heart functioning when it feels so broken? When your happiness starts to depend on somebody else, protect yourself, because you are fucked. I’m waiting for a surprise…that really defeats the purpose doesn’t it? I just saw things from his eyes, and in that moment, my heart slipped away. What is his soul making him think? Is it that I am not worthy? Maybe I am too real to be touched. Maybe that’s the problem. What would happen if I wasn’t in my own shoes anymore? If I didn’t have the life I’m living this very second? What if I didn’t know the people I love so much today? What if I didn’t do the things that I love to do in this moment? What if I wasn’t all the qualities which fill me with who I have become today? What if I didn’t feel the things I’m feeling as I’m typing this word? What if I wasn’t me? I think we are the truest versions of ourselves at night before we go to sleep, just before we close our eyes. There’s only one type of fish in this sea, and that is the selfish kind. Other people’s dreams are coming true; their memories are being created, their life is happening outside these four walls, and I am still here…I am still me. If emotions are bags, I’ve gone so far through the sadness bag, that I ripped a hole at the bottom of it. They say you have to love yourself before you love someone else. But then again, they also don’t like selfish people. D E A T H—Drop Everything And Trust Him I’m merely trying to survive until my respective death. It’s frowned upon, that the girl who once smiled through the shit, can never slip, and frown for a second. I will do a cartwheel one day, and on that day I can say, ‘I’ve made it’. I want things to not be what they are, and what they are to not be. Lie down, kick back, and listen to the sound of your heart falling in love. They are watching me like I am some TV show. They say that there is such a thing as soul mates, but mine is the only one that will die with me. I had love for you, but lost respect for myself. Why do we put those we love on a pedestal so high that it’s impossible to reach? You know you’re in love when their spark ignites the light in you. Even bad moments are moments, you know. I want to get over you, but I want you to do things, and when you do them, I love you. If you feel like things aren’t moving, there is a cure for that, and that is time. I feel like I have to keep reminding myself that this is really just a phase, and I’m going to get over it. I believe our little world is bigger and better than the entire universe in some way. If you could only know how loved you made me feel that day. People think that I should be thrilled that it’s not yesterday, just because I’m better today. These people that give me advice can breathe for themselves, and I am just not applicable. My body is sinking, and I can’t seem to find which way is up. I hope I’ll never forget you in the hopes of remembering. Why is sadness so unattractive? Sadness is attracted to me. I am sad because I am sad. I despise the feelings that come with jealousy. I dream of the littlest moments I hope to become my future. I dream of one of my aunts asking at my wedding where the one I’m going to marry is, and I whisper to her, ‘Come, let us find him’. What is living if I can’t breathe? You know you’ve won the game when the person who used to bully you and make life hell, recognises how far you’ve come, and feels badly for their earlier doings. I’ve forgotten what it’s like to wake up in the morning without feeling the insides of my lungs. It’s the worst for me. I am suffering. It is my body that is broken. If life is the show and we are the puppets, I wonder who is watching. There is no going up without going down. And in that moment I realised, if I keep him close, I fall deeper in love, and if I let him go, I will soon not remember. Which of the two makes me happier? That is the question. Being bathed head to toe by someone else at 18 years old, triggers insanity. In the end, it’s the little things that make us big. It’s funny how we take a long time to give somebody our hearts, yet, within seconds of knowing someone, we are willing to give so much of ourselves. That’s what I will never understand about this generation. Heart to heart is my favorite kind of conversation. It is the way to feel the most connected to a person. Your problems are my problems, and that is never the case. The cure for any sadness is connection with the people we love. Once you’ve lost all connection, then you know you’re losing the battle. I’m at a place so low that if anyone does anything in the slightest way to push my buttons, I become angrier than I actually should be, and it’s the scariest when it feels like you’re out of control. Having lost something so big has taught me to appreciate the littlest things. I am blessed that I have my eyes to see the vividness in the green trees. I am blessed to have my sense of smell, so I can inhale (pun intended) that particular musty stink that hangs over Delhi after a day of rain. I am blessed to have my ears, so I can listen to the sound of my mother’s laughter. I am blessed to have my lips, so I can speak to those I love. I am blessed to have my hands, so I can paint whenever I please. I am blessed to have my legs, so I can still walk on this earth. I must remember that I am blessed. I’m going through this, therefore, I am real, but what am I really real for? To think that you don’t love me is painfully disappointing. I really don’t know what I know that I don’t know. I’m so stressed that falling asleep feels like a nightmare. I think that we get really pissed at the superficial, irrelevant things, when we are really pissed at what our lives have turned into. I think that is the underlying truth. I feel sorry for myself, and then I just tell myself, ‘it’s okay’. Just being with a loved one is a real mood lifter. I like to paint my pain. I find that I do not remember the various invasive surgeries and trauma I went through as a baby, or even in the recent past. My mind has learnt to erase the pain I know I will want to forget, and for that, I am very grateful. So let’s succumb to the inevitable truth; death is upon us and we are all screwed. It’s weird how we once never knew the people we know now. I’m hanging on for dear life, literally. What is, is, and what will be will be, and what was never really was, was it? My head is the room, and my thoughts are the elephants, and I am just so awkward. Maybe sadness is unattractive, so we are conditioned to want to feel it less. Sometimes I hate myself and myself hates me too. If I look back on my early teen years, I realise I had lungs but lacked self-confidence. Now I have self-confidence but my lungs are lacking. Which of the two is better? It seems like everyone else is wearing a sugary coat, and I am the only one wearing the salty kind. It’s like I’m being sucked in by quick sand that is my disease. Some words are worth gold. Say them while you still can. Say sorry. Say I forgive you. Say thank you. Say you are welcome. Say I love you. Say I love you too. It’s the scariest when I feel my own spark start to slip away from my own body. Darkness has emerged into the light, and winter is coming. My heart is lit with a thousand fairy lights. I will never let them fuse. I must admit I am jealous of everybody I see. I see girls just standing up and chatting to one another. They look so healthy, and I would kill to be them, and just be able to stand up too. Let’s swallow our feelings because saying how we truly feel is not really the done thing these days. I don’t want to be so transparent that you know exactly what I’m thinking; yet I don’t want to come across as absolutely fake. Is there a place in between? Self-recognition is the best kind. I remember when I was little and it rained; I used to think that God was sad at the world, and the rains were his tears. Sometimes I find it easier not to talk, or even put a smile on my face, and sometimes, I think that is okay. If I was not like this, I would not have met the people I love so much today. Happiness comes in all shapes and sizes; you just have to find the one that fits you best. I do like the superficial things too. They allow me to decorate my body. Those things bring me to life, even when I don’t feel very alive. I am so weak; my only way to shout is to be strong. I felt my lungs were steadily running out of air, like it was a ticking time bomb. I don’t want to jinx it, but I should be grateful that I haven’t had three life-threatening diseases. I want to make you see the world through the eyes of my soul. Sometimes it makes me happier to hold on to a grudge than to consider letting it go. Holding on to it gives you a weird sense of power, and it almost feels like you have the upper hand. It’s my choice whether I fall into that trap or not. When I’m sad, people tend to brush it under the carpet; but I can’t do that because I am the dirt. My biggest fear of death is the notion that it is all over. The love I had for you was just another bad influence. I have come to accept the sadness within me. It’s funny how we all see common random things in the day, and connect them to our own life’s situations. We all see the same news on TV, we see the same movies, read the same books, but it means something different to each of us. We look at things from wherever we are in our lives, and move on from it thinking something entirely new. Life is full of countless perspectives. You have a way of nourishing my soul. If I didn’t have this life and I had that life, I would still pine for another life that is not my own. My thoughts have become my best friend, and I really don’t get along with them. I think over the things I over-think. It’s the little girl inside me that still wants the fantasy ending. I want to live without being pitied on. If anything, it’s wonderful to know that there are people like you in this world. A dramatic life calls for a little dramatic thinking. When I was younger, I used to think I was the sickest I was ever going to be. Today, I still think that as I lie down with the oxygen tube in my nose. What’s around the corner? I don’t ever want to know. We are in the most vulnerable state when we think we are about to die. I think we mostly get one main thought that gets stuck in our hearts. It is either, ‘I should have said this’, or ‘I should have done that, I should have been this’. If you are lucky enough to get a second chance at life, you must say it. Do it. Be it. If you can’t change your own life, there’s always someone else’s. How can I sleep on something when it is the very thing that is keeping me awake? I have come to accept the sadness that overwhelms me. The hard part aboout being determined is staying committed, and vice versa. All our thoughts get recreated in the universe, written down in God’s plan for our future. It’s only now that I can almost see what my body feels. It’s like I can already see things that are slowly being born into the truth. I felt my feet stepping closer into the relationship. Just because I was strong in one moment of weakness, doesn’t mean that I am strong enough to be in each moment. Things that are brushed under the carpet always have a way of getting stuck in our hearts forever. Love struck by your presence upon me. When you’re unwell for a really long time, it becomes your identity. Will you hold my hand and lie with me in the grass, the blue skies above, where the world is turning around us, but we are one? I never thought about my lungs when they were healthy. Pain lingers in the mind longer than it really lasts. When I feel the monotony of my day turn into sheer pain, the only thing I can really do is stop and appreciate whatever I may be doing. Just stop and listen to the words in the movie I am watching. Just stop and feel the soft fur on my dogs, and give them a million kisses. Just stop and embrace the hot water on my body in the shower. Just stop and look at my surroundings. Just stop and take in the sweet taste of my favorite candy. We should just stop for a second, because one day, we may not be able to start again. There is a lot of you in my heaven. Sometimes the jealousy really gets in the way when I want to connect with someone. Even when we are in a group setting, and not saying something, something is always being said. It’s heartbreaking to hear people talk about the future, when your first thought is to wonder if you would still be around. When is the right time to die? Sometimes I hold on to grudges, because I feel like I don’t have control over much else in my life. It’s ironic, because I want more time, yet I’m struggling to cope with a lot of it. My lungs don’t let me cry enough. Even though I’m not okay, I must remember that sometimes other people may not be okay too. Knowing all the facts doesn’t make anything easier. I should be grateful that the shit isn’t shittier than what it’s about to be. I am restlessly resting. The minute you realise you’re thinking about dying more than living, is the moment you need to change gears. We are so selfish, because we are never truly in love with another human being. We are just in love with a reflection of our desires, an idealisation of a dream, which, in the end, is merely our own. I dread falling asleep, because of those dreams that will never unfold. Has anyone else hit the bottom of this rock? What was, is not. Friends are just people you meet along the way; people who are written in your destiny, the characters in your life’s play. And suddenly I found myself caught somewhere in between not living and not dying. The thing is, I’ve been on both sides of the grass, and now I know for a fact that the other side is greener, and I’m just stuck on the less green side forever. I sleep in the late hours of dawn on purpose, in order to waste most of my next day, so that I have to kill less time. Everybody is moving with their lives in the day, life is happening outside these four walls, and I feel as if I am stuck in time. At night though, everyone is supposed to be sleeping. It is my time to be alive. Let’s rise above those who want to make us sink. Nobody realises you are dying till you are actually dead. I like to think that if someone is being remembered in a good way somewhere else in this world, they are one step closer to fulfilling their dreams. Sometimes I simply need to speak to someone to hear someone else’s voice but my own. I find it so easy to be honest, but it’s so hard to be honest about being needy. The best part is not everybody’s mind knows what your mind knows. And at that moment, I didn’t know if I was insane, or sober. I want to run out of my body. I’ve written these pages, yet I myself am afraid of what’s coming next. When you’re dying, in your mind you think everyone will soon lose you… but in your heart, you know it’s you who is going to lose everyone. Pick the highest mountain to climb on, and the dullest of the days to shine on. My heart plays little magic tricks in my dreams. I am fearless when it comes to being fearful. You are the food for my thoughts. Sometimes it gets so bad that I just want to put my hands up and yell, ‘I surrender’. Empathy is the hardest thing to give, when you think you are the one who needs it most. She never knew of the silver light that sparkled inside her, until he smiled at her and turned it on. It is so unfair. I compare and compare. The older they get, the more they can do; but the older I get, the less I can do. Honesty is only the best policy when you are certain that the other person can handle it. My head is so heavy; my thoughts probably weigh more than me. The great thing about being terminally ill is that you can say whatever the fuck you like, and not care about it being a huge deal. #nofilter His voice crept into her heart, and she no longer felt the sting of being alone. Then is not now, but now will soon be then. # Dear God, I have some unfinished business here, so if it’s okay with you, I would like to stay here as long as I possibly can. Thank you. Love, Me # When it feels like you have lost all hope, remind yourself that in time, it always has a way of being found. That is what hope is after all. And in the end, it was he who healed her open wounds he had so viciously deepened. I was in desperate need to hear that everything would be okay, as death came to say hello. My mother is an angel sent down to help me glide through the broken ice. Even though she loved, she forever hated her reality; but when it slipped away from her, she never loved again. Bubblegum makes the blues a little pinker. And that morning, my head was no longer on my shoulders, and my bones had burnt to ice. My disease gave me a feeling that I never knew I could experience; that feeling of not being human. I’d like to think that one day we will all meet up there and throw a huge party in the sky. Maybe those who I want close can’t get any closer, because they fear that I am the one who will go far too soon. And my soul weeps to the symphony of your lullaby and at 7am, I fall sound asleep like your little baby. The mind is such a strange thing once it hears something different; it shifts to a place you never knew it could go. Insanity loves profanity. As I held him dead in my arms, the fairy dust that once sparkled inside his soul froze to shattered glass, scarring my heart for eternity. And somewhere between the middle of sleeping and waking up to her dark world, she heard the voice of her angel, as he whispered from afar, ‘Now you know the feeling of grief, my darling. It has only touched upon you now that I am gone. It had to be me before you; else you wouldn’t experience this great life that everybody is living.’ His absence stained her reality with a million permanent markers. The threads that are attached to our death are the very ones that keep us alive. Maybe life is a bad dream that we only wake up from when we die. And soon I realised that my lungs had turned to stone. # I don’t know how the broken pieces of me are still sticking together, just hanging on by a piece of withered thread. This thread that was once thick and silky, becomes thinner and thinner, as God takes one more thing away from me each year. It becomes rough and raw, as I begin to realise that everything I thought I had, was never mine to begin with. I had nothing. I have nothing, and it is when this thread snaps, that I will be nothing at all. # That night she spoke to her anger; the dirty maroon ball that was burning on the inside of her knotted stomach. This is what he told her: ‘I hate God for doing this to you, and I hate anybody who pisses you off. I become bigger and bigger, the more your heart aches. I control you. I am much bigger than you, and I know you hate me. Of course you do. I am unpleasant because I simply don’t feel good in your body. But it’s okay, because I am here to teach you a lesson. Without me, you wouldn’t have anything to feed off of. You don’t know it yet, but I am your friend. You can never get rid of me, for I will always be with you. You need to crack now. You have been hiding me away for far too long with those pretty smiles and the million, “‘I’m okay’s’”. It’s my turn to shine; I am fed up of rotting inside you. Actually, maybe I am the one who is scared of you. I don’t like to see you upset. You are my friend. I’m going to come out whenever I want to. I don’t really care anymore. I know that you are strong enough to deal with it. I have won this game. I feel powerful. After all, it is me who makes you human, my darling.’ # So, let’s aim for the moon, walk in the darkness together, and catch the glittering stars along the way. Black sunshine baby Why do you hurt me so My eyes cry when it’s rainy My heart melts in the snow You tear me to shreds and bits They told me I have a sad smile My nights never again star lit Black sunshine please stay a while Thank you Thankful for my angels on earth: Aditi Chaudhary (My mother) Niren Chaudhary (My father) Ishaan Chaudhary (My brother) Kobe (My Labrador) Rita and Sandeep Kamat (My God parents) Dr. Egbert Gerritsen (My immunologist) Dr. Terrence Witt (My guardian angel) Gaya Turowicz (My guardian angel) Anja Palombo (My inspiring art teacher) Beth Miller-Manchester (My High-School protector) Virginia Holmes (My friend and mentor) Dr Avtaar Litt and the listeners of Sunrise Radio
Pages
- Index of Lessons in Technology
- Index of Book Summaries
- Index of Book Lists And Downloads
- Index For Job Interviews Preparation
- Index of "Algorithms: Design and Analysis"
- Python Course (Index)
- Data Analytics Course (Index)
- Index of Machine Learning
- Postings Index
- Index of BITS WILP Exam Papers and Content
- Lessons in Investing
- Index of Math Lessons
- Downloads
- Index of Management Lessons
- Book Requests
- Index of English Lessons
- Index of Medicines
- Index of Quizzes (Educational)
Tuesday, February 14, 2023
My Little Epiphanies (Aisha Chaudhary)
Sunday, February 12, 2023
Word Meanings 2023-Feb-13
Index of Word Meanings
1. copious /ˈkəʊpɪəs/ adjective abundant in supply or quantity. "she took copious notes" Similar: abundant superabundant plentiful ample profuse full extensive considerable substantial generous bumper lavish fulsome liberal bountiful overflowing abounding teeming in abundance many numerous multiple multifarious multitudinous manifold countless innumerable a gogo galore lank bounteous plenteous myriad Opposite: sparse ARCHAIC profuse in speech or ideas. "I had been a little too copious in talking of my country" --- 2. mimeograph /ˈmɪmɪəɡrɑːf/ noun noun: mimeograph; plural noun: mimeographs a duplicating machine which produces copies from a stencil, now superseded by the photocopier. a copy produced on a mimeograph. verb verb: mimeograph; 3rd person present: mimeographs; past tense: mimeographed; past participle: mimeographed; gerund or present participle: mimeographing make a copy of (a document) with a mimeograph. "a mimeographed letter" Origin late 19th century: formed irregularly from Greek mimeomai ‘I imitate’ + -graph. --- 3. mettlesome /ˈmɛtls(ə)m/ adjective LITERARY adjective: mettlesome (of a person or animal) full of spirit and courage; lively. "their horses were beasts of burden, not mettlesome chargers" Similar: spirited game gritty intrepid fearless courageous hardy brave plucky gallant valiant valorous bold daring audacious heroic tenacious steely determined resolved resolute steadfast indomitable Translate mettlesome to Choose language TIP Similar-sounding words mettlesome is sometimes confused with meddlesome --- 4. prescient /ˈprɛsɪənt/ Learn to pronounce adjective adjective: prescient having or showing knowledge of events before they take place. "a prescient warning" Similar: prophetic predictive visionary psychic clairvoyant far-seeing far-sighted with foresight prognostic divinatory oracular sibylline apocalyptic fateful revelatory insightful intuitive perceptive percipient foreknowing previsional vatic mantic vaticinal vaticinatory prognosticative augural adumbrative fatidic fatidical haruspical pythonic Origin early 17th century: from Latin praescient- ‘knowing beforehand’, from the verb praescire, from prae ‘before’ + scire ‘know’. --- 5. strident /ˈstrʌɪdnt/ Learn to pronounce adjective adjective: strident 1. (of a sound) loud and harsh; grating. "his voice had become increasingly strident" Similar: harsh raucous rough grating rasping jarring loud stentorian shrill screeching piercing ear-piercing unmelodious unmusical discordant dissonant unharmonious stridulous stridulant stridulatory stentorious Opposite: soft dulcet PHONETICS another term for sibilant. 2. presenting a point of view, especially a controversial one, in an excessively forceful way. "public pronouncements on the crisis became less strident" Origin mid 17th century: from Latin strident- ‘creaking’, from the verb stridere . --- 6. ebullience /ɪˈbʌlɪəns,ɪˈbʊlɪəns/ Learn to pronounce noun noun: ebullience the quality of being cheerful and full of energy; exuberance. "the ebullience of happy children" Similar: exuberance buoyancy cheerfulness joy joyfulness gladness --- 7. quixotic /kwɪkˈsɒtɪk/ Learn to pronounce adjective adjective: quixotic extremely idealistic; unrealistic and impractical. "a vast and perhaps quixotic project" h Similar: idealistic unbusinesslike romantic extravagant starry-eyed visionary utopian perfectionist unrealistic unworldly impracticable unworkable impossible non-viable inoperable unserviceable useless ineffective ineffectual inefficacious --- 8. bland /bland/ Learn to pronounce adjective adjective: bland; comparative adjective: blander; superlative adjective: blandest lacking strong features or characteristics and therefore uninteresting. "bland, mass-produced pop music" h Similar: uninteresting dull boring tedious monotonous dry drab dreary wearisome unexciting unimaginative uninspiring uninspired weak insipid colourless lustreless lacklustre vapid flat stale trite vacuous feeble pallid wishy-washy limp tired lifeless torpid unanimated zestless spiritless sterile anaemic barren tame bloodless antiseptic middle-of-the-road run-of-the-mill commonplace mediocre nondescript characterless mundane inoffensive humdrum prosaic h Opposite: interesting stimulating (of food or drink) unseasoned, mild-tasting, or insipid. "a bland and unadventurous vegetarian dish" h Similar: tasteless flavourless insipid mild savourless unflavoured weak thin watery watered-down spiceless unappetizing wishy-washy h Opposite: tangy showing no strong emotion. "his expression was bland and unreadable" h Similar: temperate mild soft calm balmy soothing benign h Opposite: violent destructive Origin late Middle English (in the sense ‘gentle in manner’): from Latin blandus ‘soft, smooth’. --- 9. veritable /ˈvɛrɪtəbl/ Learn to pronounce adjective adjective: veritable used for emphasis, often to qualify a metaphor. "the early 1970s witnessed a veritable price explosion" Origin late Middle English: from Old French, from verite ‘truth’ (see verity). Early senses included ‘true’ and ‘speaking the truth’, later ‘genuine, actual’. --- 10. haggard /ˈhaɡəd/ Learn to pronounce adjective 1. looking exhausted and unwell, especially from fatigue, worry, or suffering. "she was pale and haggard" Similar: careworn tired drained drawn raddled unwell unhealthy sickly spent sapped washed out rundown exhausted gaunt grim pinched peaked peaky hollow-cheeked hollow-eyed pale wan grey ashen pallid pasty-faced sallow thin emaciated wasted cadaverous ghastly ghostlike deathlike Opposite: fresh healthy 2. (of a hawk) caught for training as a wild adult of more than twelve months. noun a haggard hawk. --- 11. capitulation /kəˌpɪtjʊˈleɪʃn/ Learn to pronounce noun noun: capitulation; plural noun: capitulations the action of ceasing to resist an opponent or demand. "she gave a sigh of capitulation" h Similar: surrender submission yielding giving in succumbing acquiescence laying down of arms fall defeat h Opposite: resistance historical an agreement or set of conditions. --- 12. whereof /wəˈrɒv,wɛːˈrɒv,wɛˈrɒv/ Learn to pronounce adverbformal adverb: whereof of what or which. "I know whereof I speak" --- 13. vilify /ˈvɪlɪfʌɪ/ Learn to pronounce verb gerund or present participle: vilifying speak or write about in an abusively disparaging manner. "he has been vilified in the press" h Similar: disparage denigrate defame run down revile berate belittle abuse insult slight attack speak ill of speak evil of pour scorn on cast aspersions on criticize censure condemn decry denounce pillory lambast fulminate against rail against inveigh against malign slander libel spread lies about blacken the name/reputation of sully the reputation of give someone a bad name bring someone into disrepute discredit stigmatize traduce calumniate impugn slur do down do a hatchet job on take to pieces pull apart throw mud at drag through the mud have a go at hit out at jump on lay into tear into knock slam pan bash hammer roast skewer bad-mouth throw brickbats at rubbish slag off monster slate pummel dump on bag contemn derogate vituperate asperse vilipend h Opposite: commend lionize Origin late Middle English (in the sense ‘lower in value’): from late Latin vilificare, from Latin vilis ‘of low value’ (see vile). --- 14. admonition /ˌadməˈnɪʃn/ Learn to pronounce noun noun: admonition; plural noun: admonitions a firm warning or reprimand. "he received numerous admonitions for his behaviour" h Similar: reprimand rebuke reproof remonstrance reproach admonishment stricture lecture criticism recrimination tirade diatribe philippic harangue attack scolding chastisement castigation upbraiding berating reproval censure condemnation telling-off dressing-down talking-to tongue-lashing bashing blast rap rap over the knuckles slap on the wrist flea in one's ear earful roasting rollicking caning blowing-up rocket wigging slating ticking off carpeting serve rating exhortation warning caution caveat piece of advice recommendation injunction monition enjoinment instruction direction suggestion lesson precept advice counsel guidance urging encouragement persuasion pressure View 1 vulgar slang word h Opposite: commendation pat on the back praise Origin late Middle English: from Old French amonition, from Latin admonitio(n-) ‘(cautionary) reminder’ (see admonish). --- 15. plaintiff /ˈpleɪntɪf/ Learn to pronounce nounLaw noun: plaintiff; plural noun: plaintiffs a person who brings a case against another in a court of law. "the plaintiff commenced an action for damages" Origin late Middle English: from Old French plaintif ‘plaintive’ (used as a noun). The -f ending has come down through Law French; the word was originally the same as plaintive . --- 16. fulsome /ˈfʊls(ə)m/ Learn to pronounce adjective adjective: fulsome 1. complimentary or flattering to an excessive degree. "the press are embarrassingly fulsome in their appreciation" h Similar: enthusiastic ample profuse extensive generous liberal lavish glowing gushing gushy excessive extravagant overdone immoderate inordinate over-appreciative fawning ingratiating adulatory laudatory acclamatory eulogistic rapturous flattering complimentary effusive cloying unctuous saccharine sugary honeyed over the top OTT buttery encomiastic 2. of large size or quantity; generous or abundant. "the fulsome details of the later legend" Origin Middle English (in the sense ‘abundant’): from full1 + -some1. --- 17. inadvertently /ˌɪnədˈvəːt(ə)ntli/ Learn to pronounce adverb adverb: inadvertently without intention; accidentally. "his name had been inadvertently omitted from the list" h Similar: accidentally by accident unintentionally unwittingly unawares without noticing in all innocence by mistake mistakenly h Opposite: --- 18. rancorous /ˈraŋk(ə)rəs/ Learn to pronounce adjective adjective: rancorous characterized by bitterness or resentment. "sixteen miserable months of rancorous disputes" h Similar: bitter spiteful hateful resentful acrimonious malicious malevolent malign malignant hostile antipathetic venomous poisonous vindictive evil-intentioned ill-natured baleful vengeful vitriolic virulent pernicious mean nasty bitchy catty malefic maleficent h Opposite: amicable --- 19. notwithstanding /ˌnɒtwɪðˈstandɪŋ,ˌnɒtwɪθˈstandɪŋ/ preposition preposition: notwithstanding in spite of. "notwithstanding the evidence, the consensus is that the jury will not reach a verdict" h Similar: in spite of despite regardless of for all adverb adverb: notwithstanding nevertheless; in spite of this. "I didn't like it. Notwithstanding, I remained calm" h Similar: nevertheless nonetheless even so all the same in spite of this/that despite this/that after everything however still yet be that as it may having said that that said for all that just the same anyway in any event at any rate at all events when all is said and done withal howbeit conjunction conjunction: notwithstanding although; in spite of the fact that. "notwithstanding that the hall was packed with bullies, our champion played on steadily and patiently" h Similar: although in spite of the fact that despite the fact that even though though for all that Origin --- 20. kazam interjection. Used to show that something appears by magic. --- 21. nuance /ˈnjuːɑːns/ Learn to pronounce noun plural noun: nuances a subtle difference in or shade of meaning, expression, or sound. "he was familiar with the nuances of the local dialect" h Similar: fine distinction subtle distinction/difference shade shading gradation variation modulation degree subtlety nicety refinement overtone verb 3rd person present: nuances give nuances to. "the effect of the music is nuanced by the social situation of listeners" Origin late 18th century: from French, ‘shade, subtlety’, from nuer ‘to shade’, based on Latin nubes ‘cloud’. --- 22. chasten /ˈtʃeɪs(ə)n/ Learn to pronounce verb past tense: chastened; past participle: chastened (of a rebuke or misfortune) have a restraining or moderating effect on. "the director was somewhat chastened by his recent flops" h Similar: subdue humble cow squash deflate flatten bring down bring low take down a peg or two humiliate mortify restrain tame curb check cut down to size put down put someone in their place settle someone's hash archaic (especially of God) discipline; punish. Origin early 16th century: from an obsolete verb chaste, from Old French chastier, from Latin castigare ‘castigate’, from castus ‘morally pure, chaste’. --- 23. falter /ˈfɔːltə,ˈfɒltə/ verb verb: falter; 3rd person present: falters; past tense: faltered; past participle: faltered; gerund or present participle: faltering lose strength or momentum. "the music faltered, stopped, and started up again" Similar: hesitate delay drag one's feet stall think twice get cold feet change one's mind waver oscillate fluctuate vacillate be undecided be indecisive be irresolute see-saw yo-yo haver hum and haw sit on the fence dilly-dally shilly-shally pussyfoot around blow hot and cold tergiversate speak hesitantly. "‘A-Adam?’ he faltered" h Similar: stammer stutter stumble speak haltingly hesitate pause halt splutter flounder blunder fumble move unsteadily or hesitantly. "he faltered and finally stopped in mid-stride" Origin late Middle English (in the senses ‘stammer’ and ‘stagger’): perhaps from the verb fold1 (which was occasionally used of the faltering of the legs or tongue) + -ter as in totter . --- 24. ingenuity /ˌɪndʒɪˈnjuːɪti/ noun noun: ingenuity the quality of being clever, original, and inventive. "considerable ingenuity must be employed in writing software" Similar: inventiveness creativity imagination originality innovation resourcefulness enterprise insight inspiration perceptiveness perception intuition flair finesse artistry genius cleverness intelligence brilliance mastery talent skill sharpness astuteness acumen acuity sharp-wittedness quick-wittedness quickness shrewdness sophistication thinking outside the box Origin late 16th century (also in the senses ‘nobility’ and ‘ingenuousness’): from Latin ingenuitas ‘ingenuousness’, from ingenuus ‘inborn’. The current meaning arose by confusion of ingenuous with ingenious. --- 25. outflank /ˌaʊtˈflaŋk/ Learn to pronounce verb verb: outflank; 3rd person present: outflanks; past tense: outflanked; past participle: outflanked; gerund or present participle: outflanking move round the side of (an enemy) so as to outmanoeuvre them. "the Germans had sought to outflank them from the north-east" outwit. "an attempt to outflank the opposition" --- 26. fait accompli /ˌfeɪt əˈkɒmpli,ˌfɛt əˈkɒmpli/ Learn to pronounce noun noun: fait accompli; plural noun: faits accomplis a thing that has already happened or been decided before those affected hear about it, leaving them with no option but to accept it. "the results were presented to shareholders as a fait accompli" Origin mid 19th century: from French, literally ‘accomplished fact’. --- 27. thaw /θɔː/ Learn to pronounce verb past tense: thawed; past participle: thawed (of ice, snow, or another frozen substance, such as food) become liquid or soft as a result of warming up. "the river thawed and barges of food began to reach the capital" h Similar: defrost h Opposite: freeze the weather becomes warmer and causes snow and ice to melt. h Similar: melt unfreeze soften liquefy dissolve unthaw h Opposite: freeze solidify make (something) warm enough to become liquid or soft. "European exporters simply thawed their beef before unloading" (of a part of the body) become warm enough to stop feeling numb. "Riven began to feel his ears and toes thaw out" make or become friendlier or more cordial. "she thawed out sufficiently to allow a smile to appear" h Similar: become friendlier become more genial become more sociable loosen up relax become more relaxed Origin Old English thawian (verb), of West Germanic origin; related to Dutch dooien . The noun (first recorded in Middle English) developed its figurative use in the mid 19th century. --- 28. abound /əˈbaʊnd/ verb verb: abound; 3rd person present: abounds; past tense: abounded; past participle: abounded; gerund or present participle: abounding exist in large numbers or amounts. "rumours of a further scandal abound" h Similar: be plentiful be abundant be numerous proliferate superabound thrive flourish be thick on the ground grow on trees be two/ten a penny abundant plentiful superabundant considerable copious ample lavish luxuriant profuse boundless munificent bountiful prolific inexhaustible generous galore plenteous h Opposite: be scarce meagre scanty have in large numbers or amounts. "this land abounds with wildlife" h Similar: be full of overflow with teem with be packed with be crowded with be thronged with be jammed with be alive with be overrun with swarm with bristle with be bristling with be infested with be thick with be crawling with be lousy with be stuffed with be jam-packed with be chock-a-block with be chock-full of be heaving with pullulate with Origin Middle English (in the sense ‘overflow, be abundant’): from Old French abunder, from Latin abundare ‘overflow’, from ab- ‘from’ + undare ‘surge’ (from unda ‘a wave’). --- 29. hail 1 /heɪl/ verb past tense: hailed; past participle: hailed 1. hail falls. "it hailed so hard we had to stop" h Similar: beat shower rain fall pour drop pelt pepper batter bombard volley assail 2. (of a large number of objects) fall or be hurled forcefully. "missiles and bombs hail down from the sky" Origin Old English hagol, hægl (noun), hagalian (verb), of Germanic origin; related to Dutch hagel and German Hagel . hail2 /heɪl/ Learn to pronounce verb past tense: hailed; past participle: hailed 1. call out to (someone) to attract attention. "I hailed her in English" h Similar: greet salute address halloo speak to call out to shout to say hello to initiate a discussion with talk to nod to wave to smile at signal to lift one's hat to acknowledge accost approach waylay stop catch collar buttonhole nobble h Opposite: say goodbye to signal (an approaching taxi) to stop. "she raised her hand to hail a cab" h Similar: flag down wave down signal to stop gesture to stop make a sign to call to shout to summon accost 2. praise (someone or something) enthusiastically. "he has been hailed as the new James Dean" Similar: acclaim praise applaud commend rave about extol eulogize vaunt hymn lionize express approval of express admiration for pay tribute to speak highly of sing the praises of make much of glorify cheer salute exalt honour hurrah hurray toast welcome pay homage to big up ballyhoo cry up emblazon laud panegyrize Opposite: criticize condemn 3. have one's home or origins in (a place). "they hail from Turkey" Similar: come from be from be a native of have been born in originate in have one's roots in be … (by birth) live in have one's home in inhabit be an inhabitant of be settled in reside in be a resident of Origin Middle English: from the obsolete adjective hail ‘healthy’ (occurring in greetings and toasts, such as wæs hæil : see wassail), from Old Norse heill, related to hale1 and whole. --- 30. stipulate1 /ˈstɪpjʊleɪt/ verb gerund or present participle: stipulating demand or specify (a requirement), typically as part of an agreement. "he stipulated certain conditions before their marriage" h Similar: specify set down set out lay down set forth state clearly demand require insist on make a condition of make a precondition/proviso of prescribe impose provide Origin early 17th century: from Latin stipulat- ‘demanded as a formal promise’, from the verb stipulari . --- 31. subvert /səbˈvəːt/ Learn to pronounce verb verb: subvert; 3rd person present: subverts; past tense: subverted; past participle: subverted; gerund or present participle: subverting undermine the power and authority of (an established system or institution). "the case involved an attempt to subvert the rule of law" h Similar: destabilize unsettle overthrow overturn bring down bring about the downfall of topple depose oust supplant unseat dethrone disestablish dissolve disrupt wreak havoc on sabotage ruin upset destroy annihilate demolish wreck undo undermine undercut weaken impair damage corrupt pervert warp deprave defile debase distort contaminate poison embitter vitiate Origin late Middle English: from Old French subvertir or Latin subvertere, from sub- ‘from below’ + vertere ‘to turn’. --- 32. auspice /ˈɔːspɪs/ Learn to pronounce nounarchaic plural noun: auspices a divine or prophetic token. Origin mid 16th century (originally denoting the observation of bird flight in divination): from French, or from Latin auspicium, from auspex ‘observer of birds’, from avis ‘bird’ + specere ‘to look’. --- 33. plenary /ˈpliːn(ə)ri/ Learn to pronounce adjective adjective: plenary 1. (of a meeting) to be attended by all participants at a conference or assembly, who otherwise meet in smaller groups. "the House is expected to bring the legislative procedures bill to a plenary meeting" h Similar: full fully constituted general complete entire open 2. unqualified; absolute. "the disciplinary committee will have plenary powers" h Similar: unconditional unlimited unrestricted unqualified absolute complete sweeping comprehensive plenipotentiary noun noun: plenary; plural noun: plenaries a meeting or session attended by all participants at a conference or assembly. "working parties would report back to the plenary with recommendations" Origin late Middle English: from late Latin plenarius ‘complete’, from plenus ‘full’. --- 34. penultimate /pɪˈnʌltɪmət/ Learn to pronounce adjective adjective: penultimate last but one in a series of things; second last. "the penultimate chapter of the book" Origin late 17th century: from Latin paenultimus, from paene ‘almost’ + ultimus ‘last’, on the pattern of ultimate . --- 35. gourmet /ˈɡʊəmeɪ,ˈɡɔːmeɪ/ Learn to pronounce noun noun: gourmet; plural noun: gourmets a connoisseur of good food; a person with a discerning palate. h Similar: gastronome epicure epicurean connoisseur bon vivant bon viveur foodie of a kind or standard suitable for a gourmet. modifier noun: gourmet "a gourmet meal" Origin early 19th century: French, originally meaning ‘wine taster’, influenced by gourmand. --- 36. armadillo /ˌɑːməˈdɪləʊ/ Learn to pronounce noun noun: armadillo; plural noun: armadillos a nocturnal insectivorous mammal that has large claws for digging and a body covered in bony plates. Armadillos are native to Central and South America and one kind is spreading into the southern US. Origin late 16th century: from Spanish, diminutive of armado ‘armed man’, from Latin armatus, past participle of armare ‘to arm’. --- 37. ingenuity /ˌɪndʒɪˈnjuːɪti/ Learn to pronounce noun noun: ingenuity the quality of being clever, original, and inventive. "considerable ingenuity must be employed in writing software" h Similar: inventiveness creativity imagination originality innovation --- 38. corrugated /ˈkɒrəɡeɪtɪd/ Learn to pronounce adjective adjective: corrugated (of a material or surface) shaped into a series of parallel ridges and grooves so as to give added rigidity and strength. "corrugated cardboard" h Similar: ridged fluted channelled furrowed grooved crimped folded crinkled crinkly puckered creased wrinkled wrinkly crumpled rumpled striate striated corrugate /ˈkɒrʊɡeɪt/ Learn to pronounce verb past tense: corrugated; past participle: corrugated contract or cause to contract into wrinkles or folds. "Micky's brow corrugated in a simian frown" Origin late Middle English: from Latin corrugat- ‘wrinkled’, from the verb corrugare, from cor- (expressing intensive force) + rugare (from ruga ‘a wrinkle’). --- 39. edict /ˈiːdɪkt/ Learn to pronounce noun noun: edict; plural noun: edicts an official order or proclamation issued by a person in authority. "Clovis issued an edict protecting Church property" h Similar: decree order command commandment mandate proclamation pronouncement dictum dictate fiat promulgation precept law statute act enactment bill ordinance regulation rule ruling injunction manifesto ukase pronunciamento firman decretal irade rescript Origin Middle English: from Latin edictum ‘something proclaimed’, neuter past participle of edicere, from e- (variant of ex- ) ‘out’ + dicere ‘say, tell’. --- 40. undercut See definitions in: all commerce geography art tennis forestry cooking hairdressing verb verb: undercut; 3rd person present: undercuts; past tense: undercut; past participle: undercut; gerund or present participle: undercutting /ˌʌndəˈkʌt/ 1. offer goods or services at a lower price than (a competitor). "these industries have been undercut by more efficient foreign producers" h Similar: charge less than charge a lower price than undersell underbid 2. cut or wear away the part below or under (something, especially a cliff). "the base of the crag is undercut permitting walkers to pass behind the falling water" cut away material to leave (a carved design) in relief. 3. weaken; undermine. "the chairman denied his authority was being undercut" h Similar: undermine weaken impair damage sap threaten subvert sabotage ruin disrupt undo destabilize demolish wreck destroy chip away 4. Tennis strike (a ball) with backspin so that it bounces high on landing. noun noun: undercut; plural noun: undercuts /ˈʌndəkʌt/ 1. a space formed by the removal or absence of material from the lower part of something. "there may be some bigger fish in the safety of the undercut" North American a notch cut in a tree trunk to guide its fall when felled. 2. British the underside of a sirloin of beef. 3. a hairstyle in which the hair is shaved or cut very short on the sides or back of the head but left relatively long on top. "she styled her short bob into an edgy undercut" --- 41. revel /ˈrɛvl/ Learn to pronounce verb verb: revel; 3rd person present: revels; past tense: revelled; past participle: revelled; gerund or present participle: revelling; past tense: reveled; past participle: reveled; gerund or present participle: reveling enjoy oneself in a lively and noisy way, especially with drinking and dancing. "they spent the evening revelling with their guests" h Similar: celebrate make merry have a party party feast eat drink and be merry carouse roister have fun have a good time enjoy oneself go on a spree live it up whoop it up have a fling have a ball make whoopee rave paint the town red push the boat out spree h Opposite: mourn get great pleasure from (a situation or experience). "Bill said he was secretly revelling in his new-found fame" h Similar: enjoy delight in love like adore be entertained by be amused by be pleased by take pleasure in appreciate relish lap up savour luxuriate in bask in wallow in glory in gloat over feel self-satisfied about crow about get a kick out of get a thrill out of h Opposite: hate noun noun: revel; plural noun: revels lively and noisy enjoyment, especially with drinking and dancing. "late-night revels" h Similar: celebration festivity jollification merrymaking carousal carouse spree debauch bacchanal party jamboree rave shindig bash jag do rave-up knees-up jolly thrash beano beanfeast hooley crack wingding blast shivoo rage ding jollo Origin late Middle English: from Old French reveler ‘rise up in rebellion’, from Latin rebellare ‘to rebel’. --- 42. backdrop /ˈbakdrɒp/ Learn to pronounce noun noun: backdrop; plural noun: backdrops a painted cloth hung at the back of a theatre stage as part of the scenery. the setting or background for a scene, event, or situation. "the conference took place against a backdrop of increasing diplomatic activity" verb verb: backdrop; 3rd person present: backdrops; past tense: backdropped; past participle: backdropped; gerund or present participle: backdropping lie behind or beyond; serve as a background to. "the rolling hills that backdropped our camp" --- 43. ratchet1 /ˈratʃɪt/ Learn to pronounce See definitions in: all mechanics finance economics noun noun: ratchet; plural noun: ratchets 1. a device consisting of a bar or wheel with a set of angled teeth in which a cog or tooth engages, allowing motion in one direction only. "a ratchet screwdriver" a bar or wheel that forms part of a ratchet. 2. a situation or process that is perceived to be changing in a series of irreversible steps. "the upward ratchet of property taxes" verb verb: ratchet; 3rd person present: ratchets; past tense: ratcheted; past participle: ratcheted; gerund or present participle: ratcheting 1. operate by means of a ratchet. "a ratcheted quick release system" 2. cause something to rise or fall as a step in a steady and irreversible process. "the Bank of Japan ratcheted up interest rates again" rise or fall as a step in a steady and irreversible process. "the budget deficit continues to ratchet upward" Origin mid 17th century: from French rochet, originally denoting a blunt lance head, later in the sense ‘bobbin, ratchet’; related to the base of archaic rock ‘quantity of wool on a distaff for spinning’. ratchet2 /ˈratʃɪt/ Learn to pronounce adjective derogatory•informal adjective: ratchet (especially in African American usage) unattractively coarse, disreputable, or unfashionable (typically used of a woman). "she looks so ratchet in that dress" (of a thing) rough, crude, or unsophisticated. "that party was ratchet" Origin 1990s: perhaps derived from wretched or possibly from ratshit. --- 44. vitriol /ˈvɪtrɪəl/ Learn to pronounce noun noun: vitriol 1. bitter criticism or malice. "her mother's sudden gush of fury and vitriol" 2. archaic•literary sulphuric acid. "it was as if his words were spraying vitriol on her face" in names of metallic sulphates, e.g. blue vitriol (copper sulphate) and green vitriol (ferrous sulphate). Origin late Middle English (denoting the sulphate of various metals): from Old French, or from medieval Latin vitriolum, from Latin vitrum ‘glass’. --- 45. cri de cœur /ˌkriː də ˈkəː/ noun noun: cri de cœur; plural noun: cris de cœur a passionate appeal, complaint, or protest. "a patriotic cris de coeur" Origin French, ‘cry from the heart’. --- 46. shrift /ʃrɪft/ Learn to pronounce nounarchaic noun: shrift; plural noun: shrifts confession, especially to a priest. "go to shrift" absolution by a priest. Origin Old English scrift ‘penance imposed after confession’, from shrive. --- 47. absolution /ˌabsəˈluːʃn/ Learn to pronounce noun noun: absolution; plural noun: absolutions formal release from guilt, obligation, or punishment. "absolution from the sentence" h Similar: forgiveness pardoning exoneration remission dispensation indulgence purgation clemency mercy pardon reprieve discharge amnesty delivery acquittal clearing freedom liberation deliverance release condoning vindication exculpation let-off letting off shrift shriving h Opposite: punishment ecclesiastical declaration that a person's sins have been forgiven. "she had been granted absolution for her sins" Origin Old English absolutionem (after Latin), from Latin absolutio(n- ), from the verb absolvere (see absolve); subsequently reinforced by Old French absolution . --- 48. vibrissae /vʌɪˈbrɪsiː/ noun plural noun: vibrissae; noun: vibrissa Zoology long stiff hairs growing around the mouth or elsewhere on the face of many mammals, used as organs of touch; whiskers. Ornithology coarse bristle-like feathers growing around the gape of certain insectivorous birds that catch insects in flight. Origin late 17th century: from Latin, literally ‘nostril hairs’. --- 49. lucid /ˈl(j)uːsɪd/ Learn to pronounce adjective adjective: lucid 1. expressed clearly; easy to understand. "a lucid account" h Similar: intelligible comprehensible understandable cogent coherent communicative articulate eloquent clear clear-cut crystal clear transparent plain simple direct vivid sharp straightforward perspicuous unambiguous graphic explicit joined-up h Opposite: confusing unclear ambiguous showing or having the ability to think clearly, especially in intervals between periods of confusion or insanity. "he has a few lucid moments every now and then" h Similar: rational sane in one's right mind of sound mind able to think clearly normal balanced well balanced sensible clear-headed right-minded sober compos mentis all there with all one's marbles h Opposite: muddled Psychology (of a dream) experienced with the dreamer feeling awake, aware of dreaming, and able to control events consciously. 2. literary bright or luminous. "birds dipped their wings in the lucid flow of air" h Similar: bright shining gleaming luminous radiant brilliant glowing dazzling lustrous luminescent phosphorescent lucent lambent effulgent refulgent h Opposite: dark dull Origin late 16th century (in lucid (sense 2)): from Latin lucidus (perhaps via French lucide or Italian lucido ) from lucere ‘shine’, from lux, luc- ‘light’. --- 50. reprisal /rɪˈprʌɪzl/ Learn to pronounce noun plural noun: reprisals an act of retaliation. "three youths died in the reprisals which followed" h Similar: retaliation counterattack counterstroke comeback revenge vengeance retribution requital recrimination an eye for an eye a tooth for a tooth tit for tat getting even redress repayment payback lex talionis a taste of one's own medicine ultion a Roland for an Oliver historical the forcible seizure of a foreign subject or their goods as an act of retaliation. Origin late Middle English: from Anglo-Norman French reprisaille, from medieval Latin reprisalia (neuter plural), based on Latin repraehens- ‘seized’, from the verb repraehendere (see reprehend). The current sense dates from the early 18th century. --- 51. confer /kənˈfəː/ Learn to pronounce verb gerund or present participle: conferring 1. grant (a title, degree, benefit, or right). "the Minister may have exceeded the powers conferred on him by Parliament" h Similar: bestow on present with/to grant to award to decorate with honour with give to give out to gift with endow with vest in hand out to extend to vouchsafe to accord to h Opposite: withhold remove 2. have discussions; exchange opinions. "the officials were conferring with allies" h Similar: consult have discussions discuss things exchange views talk have a talk speak converse communicate have a chat have a tête-à-tête negotiate have negotiations have talks parley palaver have a confab chew the fat/rag jaw rap powwow confabulate Origin late Middle English (in the general sense ‘bring together’, also in confer (sense 2)): from Latin conferre, from con- ‘together’ + ferre ‘bring’. --- 52. yonder /ˈjɒndə/ Learn to pronounce adverbarchaic•dialect adverb: yonder at some distance in the direction indicated; over there. "there's a ford south of here, about nine miles yonder" determinerarchaic•dialect determiner: yonder that or those (used to refer to something situated at a distance). "what light through yonder window breaks?" noun noun: yonder the far distance. "attempting to fly off into the wide blue yonder" Origin Middle English: of Germanic origin; related to Dutch ginder ‘over there’, also to yon. --- 53. pigtail /ˈpɪɡteɪl/ Learn to pronounce See definitions in: all hairdressing electrical smoking noun noun: pigtail; plural noun: pigtails; noun: pig-tail; plural noun: pig-tails 1. a plaited lock of hair worn singly at the back or on each side of the head. "she had her hair done in pigtails" 2. a short length of braided wire connecting a stationary part to a moving part in an electrical device. 3. a thin twist of tobacco. ---
PySpark Books (2023 Feb)
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1. Tomasz Drabas, Denny Lee Packt Publishing Ltd, 27-Feb-2017 2. Data Analysis with Python and PySpark Jonathan Rioux Simon and Schuster, 12-Apr-2022 3. PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python Denny Lee, Tomasz Drabas Packt Publishing Ltd, 29-Jun-2018 4. Machine Learning with PySpark: With Natural Language Processing and Recommender Systems Pramod Singh Apress, 14-Dec-2018 5. Learning Spark: Lightning-Fast Big Data Analysis Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia "O'Reilly Media, Inc.", 28-Jan-2015 6. Advanced Analytics with PySpark Akash Tandon, Sandy Ryza, Sean Owen, Uri Laserson, Josh Wills "O'Reilly Media, Inc.", 14-Jun-2022 7. PySpark Recipes: A Problem-Solution Approach with PySpark2 Raju Kumar Mishra Apress, 09-Dec-2017 8. Learn PySpark: Build Python-based Machine Learning and Deep Learning Models Pramod Singh Apress, 06-Sept-2019 9. Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee "O'Reilly Media, Inc.", 16-Jul-2020 10. Advanced Analytics with Spark: Patterns for Learning from Data at Scale Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills "O'Reilly Media, Inc.", 12-Jun-2017 11. Applied Data Science Using PySpark: Learn the End-to-End Predictive Model-Building Cycle Ramcharan Kakarla, Sundar Krishnan, Sridhar Alla Apress, 2021 12. Essential PySpark for Scalable Data Analytics: A beginner's guide to harnessing the power and ease of PySpark 3 Sreeram Nudurupati Packt Publishing Ltd, 29-Oct-2021 13. Spark: The Definitive Guide: Big Data Processing Made Simple Bill Chambers, Matei Zaharia "O'Reilly Media, Inc.", 08-Feb-2018 14. Spark for Python Developers Amit Nandi Packt Publishing, 24-Dec-2015 15. Frank Kane's Taming Big Data with Apache Spark and Python Frank Kane Packt Publishing Ltd, 30-Jun-2017 16. Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming Gerard Maas, Francois Garillot "O'Reilly Media, Inc.", 05-Jun-2019 17. Data Analytics with Spark Using Python Jeffrey Aven Addison-Wesley Professional, 18-Jun-2018 18. Graph Algorithms: Practical Examples in Apache Spark and Neo4j Mark Needham, Amy E. Hodler "O'Reilly Media, Inc.", 16-May-2019 19. Spark in Action: Covers Apache Spark 3 with Examples in Java, Python, and Scala Jean-Georges Perrin Simon and Schuster, 12-May-2020 20. Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling Javier Luraschi, Kevin Kuo, Edgar Ruiz "O'Reilly Media, Inc.", 07-Oct-2019 21. High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark Holden Karau, Rachel Warren "O'Reilly Media, Inc.", 25-May-2017 22. Apache Spark in 24 Hours, Sams Teach Yourself Jeffrey Aven Sams Publishing, 31-Aug-2016Tags: List of Books,Spark,
Hands-on 5 Regression Algorithms Using Scikit-Learn
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What is Regression? When the targets are real numbers and we are trying the establish a relationship between a target and a predictor, the problem is called a “regression problem”. Example 1: Salary vs Years of Experience Example 2: Weight vs Height Regression: Predicting Bengaluru Housing Prices 1. Linear Regression (Ordinary Least Squares algorithm) 2. Polynomial Regression 3. Linear Regression using Stochastic Gradient Descent 4. Regression using Support Vector Machines 5. Regression using Decision Trees Linear Regression (Ordinary Least Squares algorithm) 1: In Linear Regression, you try to fit a line to the data. Basic Idea Behind Ordinary Least Squares Algorithm: How much predictions are deviating from the actual data? Mapping errors on the graph: >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]]) >>> # y = 1 * x_0 + 2 * x_1 + 3 >>> y = np.dot(X, np.array([1, 2])) + 3 >>> reg = LinearRegression().fit(X, y) >>> reg.score(X, y) 1.0 >>> reg.coef_ array([1., 2.]) >>> reg.intercept_ 3.0... >>> reg.predict(np.array([[3, 5]])) array([16.]) Ref: scikit-learn.org Which attributes to transform during EDA? 1. Check if you model requires numerical features and if you can make the attributes numerical. For ex, for the problem of predicting housing prices, we can convert BHK column to floating point numbers: 2 BHK -> 2 2 BHK + Study -> 2.5 3 BHK -> 3 3 BHK + Servent -> 3.5 2. What if the ‘bhk’ attribute is not given? >>> pandas_df.dropna(subset = [‘bhk’]) If we have engineered all the features, can we drop null records from all the features? >>> pandas_df.dropna(inplace = True) 2. Polynomial Regression What if your data is actually more complex than a simple straight line? Generating Polynomial Features Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2]. include_bias: bool, default=True ::: If True (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model). >>> import numpy as np >>> from sklearn.preprocessing import PolynomialFeatures >>> X = np.arange(6).reshape(3, 2) >>> X array([[0, 1], [2, 3], [4, 5]]) >>> poly = PolynomialFeatures(2) >>> poly.fit_transform(X) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6., 9.], [ 1., 4., 5., 16., 20., 25.]]) Building the Polynomial Regression Model >>> from sklearn.preprocessing import PolynomialFeatures >>> poly_features = PolynomialFeatures(degree=2, include_bias=False) >>> X_poly = poly_features.fit_transform(X) >>> X[0] array([-0.75275929]) >>> X_poly[0] Array([-0.75275929, 0.56664654]) X_poly now contains the original feature of X plus the square of this feature. Now you can fit a LinearRegression model to this extended training data: >>> lin_reg = LinearRegression() >>> lin_reg.fit(X_poly, y) >>> lin_reg.intercept_, lin_reg.coef_ (array([ 1.78134581]), array([[ 0.93366893, 0.56456263]]))Tags: Machine Learning,Technology,3. Linear Regression using Stochastic Gradient Descent
What’s Gradient Descent? Gradient Descent is a very generic optimization algorithm capable of finding optimal solutions to a wide range of problems. The general idea of Gradient Descent is to tweak parameters iteratively in order to minimize a cost function. Suppose you are lost in the mountains in a dense fog; you can only feel the slope of the ground below your feet. A good strategy to get to the bottom of the valley quickly is to go downhill in the direction of the steepest slope. This is exactly what Gradient Descent does: it measures the local gradient of the error function with regards to the parameter vector θ, and it goes in the direction of descending gradient. Once the gradient is zero, you have reached a minimum! Concretely, you start by filling θ with random values (this is called random initialization), and then you improve it gradually, taking one baby step at a time, each step attempting to decrease the cost function (e.g., the MSE), until the algorithm converges to a minimum (see the figure below). Solving the problem of Linear Regression (Using SGD) Here are the high level steps that we take in implementing a simple and naive Linear Regression model using SGD: 1. Random Initialization: Initialize the model with a line along the x-axis. 2. Calculate the error function for this line. 3. By doing minor changes (d(slope) and d(intercept)) in slope and intercept, adjust the linear model to reduce the error function. 4. Repeat steps (2) and (3) until convergence. Code from sklearn.linear_model import SGDRegressor sgd_reg = SGDRegressor(n_iter=50, penalty=None, eta0=0.1) sgd_reg.fit(X, y.ravel()) >>> sgd_reg.intercept_, sgd_reg.coef_ (array([ 4.18380366]), array([ 2.74205299]))4. Regression using Support Vector Machines
We start with explaining what SVM is and then move on to using it for regression: The fundamental idea behind SVMs is best explained with some pictures. Figures below shows part of the iris dataset. The two classes can clearly be separated easily with a straight line (they are linearly separable). The left plot shows the decision boundaries of three possible linear classifiers. The model whose decision boundary is represented by the dashed line is so bad that it does not even separate the classes properly. The other two models work perfectly on this training set, but their decision boundaries come so close to the instances that these models will probably not perform as well on new instances. In contrast, the solid line in the plot on the right represents the decision boundary of an SVM classifier; this line not only separates the two classes but also stays as far away from the closest training instances as possible. You can think of an SVM classifier as fitting the widest possible street (represented by the parallel dashed lines) between the classes. This is called large margin classification. And the circled points are your ‘support vectors’. SVM Regression As we mentioned earlier, the SVM algorithm is quite versatile: not only does it support linear and nonlinear classification, but it also supports linear and nonlinear regression. The trick is to reverse the objective: Instead of trying to fit the largest possible street between two classes while limiting margin violations, SVM Regression tries to fit as many instances as possible on the street while limiting margin violations (i.e., instances off the street). The width of the street is controlled by a hyperparameter ϵ. Figure below shows two linear SVM Regression models trained on some random linear data, one with a large margin (ϵ = 1.5) and the other with a small margin (ϵ = 0.5).5. Regression using Decision Trees
First we would explain what Decision Trees are and how they work. Binary decision trees operate by subjecting attributes to a series of binary (yes / no) decisions. Each decision leads to one of two possibilities. Each decision leads to another decision or it leads to prediction. How a Binary Decision Tree Generates Predictions? When an observation or row is passed to a nonterminal node, the row answers the node’s question. If it answers yes, the row of attributes is passed to the leaf node below and to the left of the current node. If the row answers no, the row of attributes is passed to the leaf node below and to the right of the current node. The process continues recursively until the row arrives at a terminal (that is, leaf) node where a prediction value is assigned to the row. The value assigned by the leaf node is the mean of the outcomes of the all the training observations that wound up in the leaf node. Below is the Decision Tree for Iris Dataset. Simple Psuedo Code for ‘Regression Using Decision Tree’ Only For The Purpose of Demonstration. Step 1: Find avarage value for interval of x and y. Let us call these values XA abd YA. Step 2: Split the curve into two by drawing a vertical line. Step 3: For x < XA, choose the average values of (x, y) from left side, drawing a horizontal line passing from this point on the left side. Step 4: For x > XA, choose the average values of (x, y) from right side, drawing a horizontal line passing from this point on the left side. Repeat steps (1) to (4) for (n-1) times where n is the depth you want in your decision tree. Moving on to Regression. Below is our sample data: Block diagram of depth 1 tree for simple problem Comparison of predictions and actual values versus attribute for simple example Notice how the predicted value for each region is always the average target value of the instances in that region. The algorithm splits each region in a way that makes most training instances as close as possible to that predicted value. DecisionTreeRegressor using sklearn from sklearn.tree import DecisionTreeRegressor tree_reg = DecisionTreeRegressor(max_depth=2) tree_reg.fit(X, y)References
1. Linear Regression (Ordinary Least Squares algorithm) 1.1. linear-regression-theory 1.2. penalized linear regression 2, 3, 4: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Book by Aurelien Geron 5: Machine Learning in Python (Essential Techniques For Predictive Analysis) By: Michael Bowles
Thursday, February 9, 2023
Machine Learning Books (Mar 2020)
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Putting the books listed below into three categories based on complexity
I: Mathematical Theory
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1. Deep Learning
Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio -
5. Pattern Recognition and Machine Learning
Book by Christopher Bishop -
8. Understanding Machine Learning: From Theory to Algorithms
Textbook by Shai Ben-David and Shai Shalev-Shwartz
II: Mix of Theory and Applied Study
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2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Concepts, Tools, and Techniques to Build Intelligent Systems
Book by Aurelien Geron
III: Applied Study
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6. The Hundred-Page Machine Learning Book
Book by Andriy Burkov -
9. Machine Learning for Absolute Beginners: A Plain English Introduction
Book by O. Theobald -
52. Machine Learning in Python (Essential Techniques For Predictive Analysis)
By: Michael Bowles -
53. Fifty Algorithms Every Programmer Should Know (2e)
By: Imran Ahmad (PhD) -
54. Applied Machine Learning and AI for Engineers
Solve Business Problems That Can't Be Solved Algorithmically (Release 1)
Jeff Prosise
O’Reilly Media, Inc. (2022)
Tags: List of Books,Machine Learning,All
1. Deep Learning Book by Aaron Courville, Ian Goodfellow, and Yoshua Bengio 2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems Book by Aurelien Geron 3. The Elements of Statistical Learning Book by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie 4. An Introduction to Statistical Learning: With Applications in R Book 5. Pattern Recognition and Machine Learning Book by Christopher Bishop 6. The Hundred-Page Machine Learning Book Book by Andriy Burkov 7. Deep Learning with Python Book by François Chollet 8. Understanding Machine Learning: From Theory to Algorithms Textbook by Shai Ben-David and Shai Shalev-Shwartz 9. Machine Learning for Absolute Beginners: A Plain English Introduction Book by O. Theobald 10. Python Machine Learning Book by Sebastian Raschka 11. Artificial Intelligence: A Modern Approach Textbook by Peter Norvig and Stuart J. Russell 12. Introduction to Machine Learning Textbook by Ethem Alpaydın 13. Machine Learning: A Probabilistic Perspective Textbook by Kevin P. Murphy 14. Machine Learning for Hackers Book by Drew Conway and John Myles White 15. Programming Collective Intelligence Book: O'Reilly 16. Machine Learning For Dummies Book by John Mueller and Luca Massaron 17. Bayesian Reasoning and Machine Learning Book by David Barber 18. Reinforcement Learning: An Introduction Book by Andrew Barto and Richard S. Sutton 19. Learning from Data: A Short Course Book by Hsuan-Tien Lin, Malik Magdon-Ismail, and Yaser Abu-Mostafa 20. Machine Learning in Action Book by Peter Harrington 21. Machine Learning: The Art and Science of Algorithms that Make Sense of Data Book by Peter Flach 22. Introduction to Machine Learning with Python A Guide for Data Scientists Author(s): Andreas C. Müller, Sarah Guido Publisher: O’Reilly Media, Year: 2016 23. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies Textbook by Aoife D'Arcy, Brian Mac Namee, and John D. Kelleher 24. Mining of Massive Datasets Book by Anand Rajaraman and Jeffrey Ullman 25. Foundations of Machine Learning Textbook by Afshin Rostamizadeh, Ameet Talwalkar, and Mehryar Mohri 26. Superintelligence: Paths, Dangers, Strategies Book by Nick Bostrom 27. Make Your Own Neural Network: A Gentle Journey Through the Mathematics ... Book by Tariq Rashid 28. Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-learn, and TensorFlow 2, 3rd Edition Book by Sebastian Raschka and Vahid Mirjalili 29. Machine Learning: An Algorithmic Perspective Book by Stephen Marsland 30. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World Book by Pedro Domingos 31. Grokking Deep Learning Book by Andrew W. Trask 32. Advances in Financial Machine Learning Book by Marcos Lopez de Prado 33. Machine Learning: A Guide to Current Research Book by Tom M. Mitchell 34. Pattern Classification Book by David G. Stork, Peter E. Hart, and Richard O. Duda 35. Building Machine Learning Systems with Python - Second Edition Book by Luis Pedro Coelho and Willi Richert 36. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference Book by Cameron Davidson-Pilon 37. Information Theory, Inference and Learning Algorithms Textbook by David J. C. MacKay 38. Probabilistic Graphical Models: Principles and Techniques Book by Daphne Koller and Nir Friedman 39. Interpretable Machine Learning Book by Christoph Molnar 40. The Book of Why: The New Science of Cause and Effect Book by Dana Mackenzie and Judea Pearl 41. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms Book by Nicholas Locascio and Nikhil Buduma 42. Deep Reinforcement Learning Hands-On: Apply Modern RL Methods, with Deep Q-networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More 43. Think Stats Book by Allen B. Downey 44. Gaussian Processes for Machine Learning Book by Carl Edward Rasmussen and Christopher K. I. Williams 45. Data Mining: Practical Machine Learning Tools and Techniques Book 46. Machine Learning with R Book by Brett Lantz 47. Python Data Science Handbook: Essential Tools for Working with Data Book by Jake VanderPlas 48. Real world machine learning: video edition Book by Henrik Brink, Joseph Richards, and Mark Fetherolf 49. Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning Book by Giuseppe Bonaccorso 50. Machine Learning: A Bayesian and Optimization Perspective Book by Sergios Theodoridis 51. Mathematics for Machine Learning Textbook by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth 52. Machine Learning in Python (Essential Techniques For Predictive Analysis) By: Michael Bowles 53. Fifty Algorithms Every Programmer Should Know (2e) By: Imran Ahmad (PhD) 54. Applied Machine Learning and AI for Engineers Solve Business Problems That Can't Be Solved Algorithmically (Release 1) Jeff Prosise O’Reilly Media, Inc. (2022)
Wednesday, February 8, 2023
Spark SQL in Images
Tags: Spark,Technology,1. Spark's components
2. Spark SQL Architecture
3. SQL Data Types
4. Spark's context objects
5. File Formats Supported By Spark
6. SQL Workflow
7. Catalyst Optimizer
Below steps explain the workflow of the catalyst optimizer: 1. Analyzing a logical plan with the metadata 2. Optimizing the logical plan 3. Creating multiple physical plans 4. Analyzing the plans and finding the most optimal physical plan 5. Converting the physical plan to RDDs
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