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deep learning ian goodfellow yoshua bengio aaron courville | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 1 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents website vii acknowledgments viii notation xi 1 introduction 1 1. 1 who should read this book?.................... 8 1. 2 historical trends in deep learning................. 11 i applied math and machine learning basics 29 2 linear algebra 31 2. 1 scalars, vectors, matrices and tensors............... 31 2. 2 mu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 2 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.......................... 39 2. 6 special kinds of matrices and vectors............... 40 2. 7 eigendecomposition.......................... 42 2. 8 singular value decomposition.................... 44 2. 9 the moore - penrose pseudoinverse.................. 45 2. 10 the trace operator......................... 46 2. 11 ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 2 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.... 47 2. 12 example : principal components analysis............. 48 3 probability and information theory 53 3. 1 why probability?........................... 54 i | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 2 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents 3. 2 random variables.......................... 56 3. 3 probability distributions....................... 56 3. 4 marginal probability......................... 58 3. 5 conditional probability....................... 59 3. 6 the chain rule of conditional probabilities............ 59 3. 7 independence and conditio... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 3 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
......... 62 3. 10 useful properties of common functions.............. 67 3. 11 bayes ’ rule.............................. 70 3. 12 technical details of continuous variables............. 71 3. 13 information theory.......................... 73 3. 14 structured probabilistic models................... 75 4 numerical comp... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 3 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
................. 82 4. 3 gradient - based optimization.................... 82 4. 4 constrained optimization...................... 93 4. 5 example : linear least squares................... 96 5 machine learning basics 98 5. 1 learning algorithms......................... 99 5. 2 capacity, overfitting and underfitting........ | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 3 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
................. 122 5. 5 maximum likelihood estimation.................. 131 5. 6 bayesian statistics.......................... 135 5. 7 supervised learning algorithms................... 140 5. 8 unsupervised learning algorithms................. 146 5. 9 stochastic gradient descent..................... 151 5. 10 buil... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 3 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.. 155 ii deep networks : modern practices 166 6 deep feedforward networks 168 6. 1 example : learning xor....................... 171 6. 2 gradient - based learning....................... 177 ii | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 3 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents 6. 3 hidden units............................. 191 6. 4 architecture design.......................... 197 6. 5 back - propagation and other [UNK] algorithms..... 204 6. 6 historical notes............................ 224 7 regularization for deep learning 228 7. 1 parameter norm penalties...................... ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 4 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
..................... 240 7. 5 noise robustness........................... 242 7. 6 semi - supervised learning...................... 243 7. 7 multi - task learning......................... 244 7. 8 early stopping............................ 246 7. 9 parameter tying and parameter sharing.............. 253 7. 10 sparse r... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 4 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
7. 11 bagging and other ensemble methods............... 256 7. 12 dropout................................ 258 7. 13 adversarial training......................... 268 7. 14 tangent distance, tangent prop, and manifold tangent classifier 270 8 optimization for training deep models 274 8. 1 how learning [UNK] from pure opt... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 4 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.............. 301 8. 5 algorithms with adaptive learning rates............. 306 8. 6 approximate second - order methods................ 310 8. 7 optimization strategies and meta - algorithms........... 317 9 convolutional networks 330 9. 1 the convolution operation..................... 331 9. 2 motivation................ | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 4 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##ing as an infinitely strong prior....... 345 9. 5 variants of the basic convolution function............ 347 9. 6 structured outputs.......................... 358 9. 7 data types.............................. 360 9. 8 [UNK] convolution algorithms.................. 362 9. 9 random or unsupervised features................. | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 4 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents 9. 10 the neuroscientific basis for convolutional networks....... 364 9. 11 convolutional networks and the history of deep learning.... 371 10 sequence modeling : recurrent and recursive nets 373 10. 1 unfolding computational graphs.................. 375 10. 2 recurrent neural networks..................... 378 ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 5 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.... 398 10. 6 recursive neural networks...................... 400 10. 7 the challenge of long - term dependencies............. 401 10. 8 echo state networks......................... 404 10. 9 leaky units and other strategies for multiple time scales.... 406 10. 10 the long short - term memory and other gated rnns........ | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 5 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
....................... 422 11. 2 default baseline models....................... 425 11. 3 determining whether to gather more data............ 426 11. 4 selecting hyperparameters...................... 427 11. 5 debugging strategies......................... 436 11. 6 example : multi - digit number recognition.............. | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 5 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
computer vision........................... 452 12. 3 speech recognition.......................... 458 12. 4 natural language processing.................... 461 12. 5 other applications.......................... 478 iii deep learning research 486 13 linear factor models 489 13. 1 probabilistic pca and factor analysis...... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 5 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
............... 493 13. 4 sparse coding............................. 496 iv | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 5 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents 13. 5 manifold interpretation of pca................... 499 14 autoencoders 502 14. 1 undercomplete autoencoders.................... 503 14. 2 regularized autoencoders...................... 504 14. 3 representational power, layer size and depth........... 508 14. 4 stochastic encoders and decoders................ | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 6 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
............. 515 14. 7 contractive autoencoders...................... 521 14. 8 predictive sparse decomposition.................. 523 14. 9 applications of autoencoders.................... 524 15 representation learning 526 15. 1 greedy layer - wise unsupervised pretraining........... 528 15. 2 transfer learning and d... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 6 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
...... 546 15. 5 exponential gains from depth................... 553 15. 6 providing clues to discover underlying causes.......... 554 16 structured probabilistic models for deep learning 558 16. 1 the challenge of unstructured modeling.............. 559 16. 2 using graphs to describe model structure............. 563 1... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 6 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
inference................ 584 16. 7 the deep learning approach to structured probabilistic models 585 17 monte carlo methods 590 17. 1 sampling and monte carlo methods................ 590 17. 2 importance sampling......................... 592 17. 3 markov chain monte carlo methods................ 595 17. 4 gibbs sampli... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 6 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents 18. 3 pseudolikelihood........................... 615 18. 4 score matching and ratio matching................ 617 18. 5 denoising score matching...................... 619 18. 6 noise - contrastive estimation.................... 620 18. 7 estimating the partition function.................. 623 19 approximate in... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 7 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
......... 634 19. 3 map inference and sparse coding................. 635 19. 4 variational inference and learning................. 638 19. 5 learned approximate inference................... 651 20 deep generative models 654 20. 1 boltzmann machines......................... 654 20. 2 restricted boltzmann machines.......... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 7 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
.................. 663 20. 5 boltzmann machines for real - valued data............. 676 20. 6 convolutional boltzmann machines................. 683 20. 7 boltzmann machines for structured or sequential outputs.... 685 20. 8 other boltzmann machines..................... 686 20. 9 back - propagation through random operat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 7 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
711 20. 12 generative stochastic networks................... 714 20. 13 other generation schemes...................... 716 20. 14 evaluating generative models.................... 717 20. 15 conclusion............................... 720 bibliography 721 index 777 vi | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 7 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
website www. deeplearningbook. org this book is accompanied by the above website. the website provides a variety of supplementary material, including exercises, lecture slides, corrections of mistakes, and other resources that should be useful to both readers and instructors. vii | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 8 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
acknowledgments this book would not have been possible without the contributions of many people. we would like to thank those who commented on our proposal for the book and helped plan its contents and organization : guillaume alain, kyunghyun cho, caglar gulcehre, david krueger, hugo larochelle, razvan pascanu and tho... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 9 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, frederic francis, nando de freitas, caglar gulcehre, jurgen van gael, javier alonso garcia, jonathan hunt, gopi jeyaram, chingiz kabytayev, lukasz kaiser, varun kanade, asifullah khan, akiel khan, john king, diederik p. kingma, yann lecun, rudolf mathey, matias mattamala, abhinav maurya, kevin murphy, oleg murk, roma... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 9 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##tskever, carles gelada saez, graham taylor, valentin tolmer, massimiliano tomassoli, an tran, shubhendu trivedi, alexey umnov, vincent vanhoucke, marco visentini - scarzanella, martin vita, david warde - farley, dustin webb, kelvin xu, wei xue, ke yang, li yao, zygmunt zajac and ozan caglayan. we would also like to t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 9 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents charlie gorichanaz, brendan loudermilk, eric morris, cosmin parvulescu and alfredo solano. • chapter, : amjad almahairi, nikola banic, kevin bennett, 2 linear algebra philippe castonguay, oscar chang, eric fosler - lussier, andrey khalyavin, sergey oreshkov, istvan petras, dennis prangle, thomas rohee, gitanja... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 10 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
basics nikhil garg, makoto otsuka, bob pepin, philip popien, emmanuel rayner, peter shepard, kee - bong song, zheng sun and andy wu. • chapter, 6 deep feedforward networks : uriel berdugo, fabrizio bottarel, elizabeth burl, ishan durugkar, [UNK], jong wook kim, david krueger and aditya kumar praharaj. • chapter, : mort... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 10 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##n - stantin divilov, eric jensen, mehdi mirza, alex paino, marjorie sayer, ryan stout and wentao wu. • chapter, 10 sequence modeling : recurrent and recursive nets : gokcen eraslan, steven hickson, razvan pascanu, lorenzo von ritter, rui rodrigues, dmitriy serdyuk, dongyu shi and kaiyu yang. • chapter, : daniel becks... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 10 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents • chapter, : kunal ghosh. 15 representation learning • chapter, : minh le 16 structured probabilistic models for deep learning and anton varfolom. • chapter, 18 confronting the partition function : sam bowman. • chapter, : yujia bao. 19 approximate inference • chapter, 20 deep generative models : nicolas chapa... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 11 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
writing of the book as well as for help with proofreading. we would like to thank the google brain team for providing an intellectual environment where ian could devote a tremendous amount of time to writing this book and receive feedback and guidance from colleagues. we would especially like to thank ian ’ s former ma... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 11 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
notation this section provides a concise reference describing the notation used throughout this book. if you are unfamiliar with any of the corresponding mathematical concepts, we describe most of these ideas in chapters 2 – 4. numbers and arrays a a scalar ( integer or real ) a a vector a a matrix a a tensor in identi... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 12 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents sets and graphs a a set r the set of real numbers { } 0 1, the set containing 0 and 1 { } 0 1,,..., n the set of all integers between and 0 n [ ] a, b the real interval including and a b ( ] a, b the real interval excluding but including a b a b \ set subtraction, i. e., the set containing the ele - ments of t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 13 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents calculus dy dx derivative of with respect to y x ∂y ∂x partial derivative of with respect to y x ∇xy gradient of with respect to y x ∇x y matrix derivatives of with respect to y x ∇xy tensor containing derivatives of y with respect to x ∂f ∂x jacobian matrix j ∈rm n × of f : rn →rm ∇2 xf f f ( ) ( x or h ) ( )... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 14 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
has distribution [UNK] p [UNK] [ ( ) ] ( ) ( ) ( ) f x or ef x expectation of f x with respect to p x var ( ( ) ) f x variance of under x f x ( ) p ( ) cov ( ( ) ( ) ) f x, g x covariance of and under x f x ( ) g x ( ) p ( ) h ( ) x shannon entropy of the random variable x dkl ( ) p q kullback - leibler divergence of p... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 14 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
contents functions f f : a b → the function with domain and range a b f g f g [UNK] composition of the functions and f ( ; ) x θ a function of x parametrized by θ. ( sometimes we write f ( x ) and omit the argument θ to lighten notation ) log x x natural logarithm of σ x ( ) logistic sigmoid, 1 1 + exp ( ) −x ζ x x ( )... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 15 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
array element - wise. for example, if c = σ ( x ), then c i, j, k = σ ( xi, j, k ) for all valid values of, and. i j k datasets and distributions p data the data generating distribution [UNK] the empirical distribution defined by the training set x a set of training examples x ( ) i the - th example ( input ) from a dat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 15 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1 introduction inventors have long dreamed of creating machines that think. this desire dates back to at least the time of ancient greece. the mythical figures pygmalion, daedalus, and hephaestus may all be interpreted as legendary inventors, and galatea, talos, and pandora may all be regarded as artificial life ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 16 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
[UNK] for human beings but relatively straight - forward for computers — problems that can be described by a list of formal, math - ematical rules. the true challenge to artificial intelligence proved to be solving the tasks that are easy for people to perform but hard for people to describe formally — problems that we ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 16 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction concepts are built on top of each other, the graph is deep, with many layers. for this reason, we call this approach to ai. deep learning many of the early successes of ai took place in relatively sterile and formal environments and did not require computers to have much knowledge about the worl... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 17 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the easiest for a computer. computers have long been able to defeat even the best human chess player, but are only recently matching some of the abilities of average human beings to recognize objects or speech. a person ’ s everyday life requires an immense amount of knowledge about the world. much of this knowledge is... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 17 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
statements in a language called cycl. these statements are entered by a [UNK] human supervisors. it is an unwieldy process. people struggle to devise formal rules with enough complexity to accurately describe the world. for example, cyc failed to understand a story about a person named fred shaving in the morning (, ).... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 17 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction introduction of machine learning allowed computers to tackle problems involving knowledge of the real world and make decisions that appear subjective. a simple machine learning algorithm called logistic regression can determine whether to recommend cesarean delivery ( mor - yosef 1990 et al., ).... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 18 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##istic regression was given an mri scan of the patient, rather than the doctor ’ s formalized report, it would not be able to make useful predictions. individual pixels in an mri scan have negligible correlation with any complications that might occur during delivery. this dependence on representations is a general ph... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 18 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##cation from sound is an estimate of the size of speaker ’ s vocal tract. it therefore gives a strong clue as to whether the speaker is a man, woman, or child. however, for many tasks, it is [UNK] to know what features should be extracted. for example, suppose that we would like to write a program to detect cars in ph... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 18 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction figure 1. 1 : example of [UNK] representations : suppose we want to separate two categories of data by drawing a line between them in a scatterplot. in the plot on the left, we represent some data using cartesian coordinates, and the task is impossible. in the plot on the right, we represent the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 19 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and [UNK] ; it can take decades for an entire community of researchers. the quintessential example of a representation learning algorithm is the au - toencoder. an autoencoder is the combination of an encoder function that converts the input data into a [UNK] representation, and a decoder function that converts the new... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 19 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction quantities that are directly observed. instead, they may exist either as unobserved objects or unobserved forces in the physical world that [UNK] observable quantities. they may also exist as constructs in the human mind that provide useful simplifying explanations or inferred causes of the obse... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 20 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. the shape of the car ’ s silhouette depends on the viewing angle. most applications require us to the factors of variation and discard the disentangle ones that we do not care about. of course, it can be very [UNK] to extract such high - level, abstract features from raw data. many of these factors of variation, such... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 20 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
in turn defined in terms of edges. the quintessential example of a deep learning model is the feedforward deep network or multilayer perceptron ( mlp ). a multilayer perceptron is just a mathematical function mapping some set of input values to output values. the function is formed by composing many simpler functions. w... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 20 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction visible layer ( input pixels ) 1st hidden layer ( edges ) 2nd hidden layer ( corners and contours ) 3rd hidden layer ( object parts ) car person animal output ( object identity ) figure 1. 2 : illustration of a deep learning model. it is [UNK] for a computer to understand the meaning of raw sens... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 21 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
for explaining the relationships in the observed data. the images here are visualizations of the kind of feature represented by each hidden unit. given the pixels, the first layer can easily identify edges, by comparing the brightness of neighboring pixels. given the first hidden layer ’ s description of the edges, the s... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 21 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction x1 x1 σ w1 w1 × x2 x2 w2 w2 × + element set + × σ x w element set logistic regression logistic regression figure 1. 3 : illustration of computational graphs mapping an input to an output where each node performs an operation. depth is the length of the longest path from input to output but depen... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 22 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
explain the input. the representation also stores state information that helps to execute a program that can make sense of the input. this state information could be analogous to a counter or pointer in a traditional computer program. it has nothing to do with the content of the input specifically, but it helps the mode... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 22 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
same architecture. another approach, used by deep probabilistic models, regards the depth of a model as being not the depth of the computational graph but the depth of the graph describing how concepts are related to each other. in this case, the depth 7 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 22 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction of the flowchart of the computations needed to compute the representation of each concept may be much deeper than the graph of the concepts themselves. this is because the system ’ s understanding of the simpler concepts can be refined given information about the more complex concepts. for example... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 23 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, there is no single correct value for the depth of an architecture, just as there is no single correct value for the length of a computer program. nor is there a consensus about how much depth a model requires to qualify as “ deep. ” however, deep learning can safely be regarded as the study of models that either invo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 23 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
representations computed in terms of less abstract ones. figure illustrates the relationship between these [UNK] 1. 4 ai disciplines. figure gives a high - level schematic of how each works. 1. 5 1. 1 who should read this book? this book can be useful for a variety of readers, but we wrote it with two main target audie... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 23 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction ai machine learning representation learning deep learning example : knowledge bases example : logistic regression example : shallow autoencoders example : mlps figure 1. 4 : a venn diagram showing how deep learning is a kind of representation learning, which is in turn a kind of machine learning... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 24 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction input hand - designed program output input hand - designed features mapping from features output input features mapping from features output input simple features mapping from features output additional layers of more abstract features rule - based systems classic machine learning representation... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 25 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction many software disciplines including computer vision, speech and audio processing, natural language processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. this book has been organized into three parts in order to best accommodate a variety ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 26 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
familiarity with programming, a basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory. 1. 2 historical trends in deep learning it is easiest to understand deep learning with some historical context. rather than providing a det... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 26 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 1. introduction part i : applied math and machine learning basics 2. linear algebra 3. probability and information theory 4. numerical computation 5. machine learning basics part ii : deep networks : modern practices 6. deep feedforward networks 7. regularization 8. optimization 9. cnns 10. rnns... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 27 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 1. 2. 1 the many names and changing fortunes of neural net - works we expect that many readers of this book have heard of deep learning as an exciting new technology, and are surprised to see a mention of “ history ” in a book about an emerging field. in fact, deep learning dates back to the 1940... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 28 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
under the name deep learning beginning in 2006. this is quantitatively illustrated in figure. 1. 7 some of the earliest learning algorithms we recognize today were intended to be computational models of biological learning, i. e. models of how learning happens or could happen in the brain. as a result, one of the names ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 28 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
its functionality. another perspective is that it would be deeply interesting to understand the brain and the principles that underlie human intelligence, so machine learning models that shed light on these basic scientific questions are useful apart from their ability to solve engineering applications. the modern term ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 28 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 1940 1950 1960 1970 1980 1990 2000 year 0. 000000 0. 000050 0. 000100 0. 000150 0. 000200 0. 000250 frequency of word or phrase cybernetics ( connectionism + neural networks ) figure 1. 7 : the figure shows two of the three historical waves of artificial neural nets research, as measured by the fr... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 29 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
(, ) to train a neural network with one or two rumelhart et al. 1986a hidden layers. the current and third wave, deep learning, started around 2006 ( hinton et al. et al. et al., ; 2006 bengio, ; 2007 ranzato, ), and is just now appearing in book 2007a form as of 2016. the other two waves similarly appeared in book for... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 29 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction the earliest predecessors of modern deep learning were simple linear models motivated from a neuroscientific perspective. these models were designed to take a set of n input values x1,..., xn and associate them with an output y. these models would learn a set of weights w1,..., wn and compute the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 30 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the human operator. in the 1950s, the perceptron ( rosenblatt 1958 1962,, ) became the first model that could learn the weights defining the categories given examples of inputs from each category. the adaptive linear element ( adaline ), which dates from about the same time, simply returned the value of f ( x ) itself to... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 30 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
they are trained in [UNK] ways than the original models were trained. linear models have many limitations. most famously, they cannot learn the xor function, where f ( [ 0, 1 ], w ) = 1 and f ( [ 1, 0 ], w ) = 1 but f ( [ 1, 1 ], w ) = 0 and f ( [ 0, 0 ], w ) = 0. critics who observed these flaws in linear models caused... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 30 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction well - studied parts of the brain (, ). olshausen and field 2005 neuroscience has given us a reason to hope that a single deep learning algorithm can solve many [UNK] tasks. neuroscientists have found that ferrets can learn to “ see ” with the auditory processing region of their brain if their b... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 31 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
is inspired by the brain. the neocognitron ( fukushima 1980, ) introduced a powerful model architecture for processing images that was inspired by the structure of the mammalian visual system and later became the basis for the modern convolutional network (, ), as we will see in lecun et al. 1998b section. most neural ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 31 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
compute very [UNK] functions than modern rectified linear units, but greater neural realism has not yet led to an improvement in machine learning performance. also, while neuroscience has successfully inspired several neural network architectures, we do not yet know enough about biological learning for neuroscience to [... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 31 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction neuroscience at all. it is worth noting that the [UNK] to understand how the brain works on an algorithmic level is alive and well. this endeavor is primarily known as “ computational neuroscience ” and is a separate field of study from deep learning. it is common for researchers to move back and... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 32 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
despite their popularity, symbolic models were [UNK] to explain in terms of how the brain could actually implement them using neurons. the connectionists began to study models of cognition that could actually be grounded in neural implementations ( touretzky and minton 1985, ), reviving many ideas dating back to the wo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 32 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
birds and these objects can each be red, green, or blue. one way of representing these inputs would be to have a separate neuron or hidden unit that activates for each of the nine possible combinations : red truck, red car, red bird, green truck, and so on. this requires nine [UNK] neurons, and each neuron must indepen... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 32 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction learn about redness from images of cars, trucks and birds, not only from images of one specific category of objects. the concept of distributed representation is central to this book, and will be described in greater detail in chapter. 15 another major accomplishment of the connectionist movement... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 33 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
short - term memory or lstm network to resolve some of these [UNK]. today, the lstm is widely used for many sequence modeling tasks, including many natural language processing tasks at google. the second wave of neural networks research lasted until the mid - 1990s. ven - tures based on neural networks and other ai tec... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 33 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
al. 1998b bengio et al. 2001 for advanced research ( cifar ) helped to keep neural networks research alive via its neural computation and adaptive perception ( ncap ) research initiative. this program united machine learning research groups led by [UNK] hinton at university of toronto, yoshua bengio at university of mo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 33 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 2006. [UNK] hinton showed that a kind of neural network called a deep belief network could be [UNK] trained using a strategy called greedy layer - wise pre - training (, ), which will be described in more detail in section. hinton et al. 2006 15. 1 the other cifar - [UNK] research groups quickly... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 34 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##formed competing ai systems based on other machine learning technologies as well as hand - designed functionality. this third wave of popularity of neural networks continues to the time of this writing, though the focus of deep learning research has changed dramatically within the time of this wave. the third wave be... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 34 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. fortunately, the amount of skill required reduces as the amount of training data increases. the learning algorithms reaching human performance on complex tasks today are nearly identical to the learning algorithms that struggled to solve toy problems in the 1980s, though the models we train with these algorithms have... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 34 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction into a dataset appropriate for machine learning applications. the age of “ big data ” has made machine learning much easier because the key burden of statistical estimation — generalizing well to new data after observing only a small amount of data — has been considerably lightened. as of 2016, ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 35 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
animals become intelligent when many of their neurons work together. an individual neuron or small collection of neurons is not particularly useful. biological neurons are not especially densely connected. as seen in figure, 1. 10 our machine learning models have had a number of connections per neuron that was within an... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 35 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
current artificial neurons, so biological neural networks may be even larger than this plot portrays. in retrospect, it is not particularly surprising that neural networks with fewer neurons than a leech were unable to solve sophisticated artificial intelligence prob - lems. even today ’ s networks, which we consider qui... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 35 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 1900 1950 1985 2000 2015 100 101 102 103 104 105 106 107 108 109 dataset size ( number examples ) iris mnist public svhn imagenet cifar - 10 imagenet10k ilsvrc 2014 sports - 1m rotated t vs. c t vs. g vs. f criminals canadian hansard wmt figure 1. 8 : dataset sizes have increased greatly over ti... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 36 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and 1990s, machine learning became more statistical in nature and began to leverage larger datasets containing tens of thousands of examples such as the mnist dataset ( shown in figure ) of scans 1. 9 of handwritten numbers (, ). in the first decade of the 2000s, more lecun et al. 1998b sophisticated datasets of this sam... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 36 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
- 1m dataset ( 2014a karpathy, ). at the top of the 2014 graph, we see that datasets of translated sentences, such as ibm ’ s dataset constructed from the canadian hansard (, ) and the wmt 2014 english to french brown et al. 1990 dataset ( schwenk 2014, ) are typically far ahead of other dataset sizes. 21 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 36 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction figure 1. 9 : example inputs from the mnist dataset. the “ nist ” stands for national institute of standards and technology, the agency that originally collected this data. the “ m ” stands for “ modified, ” since the data has been preprocessed for easier use with machine learning algorithms. the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 37 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction the advent of general purpose gpus ( described in section ), faster network 12. 1. 2 connectivity and better software infrastructure for distributed computing, is one of the most important trends in the history of deep learning. this trend is generally expected to continue well into the future. ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 38 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
two kinds of objects ( or in some cases, the absence or presence of a single kind of object ), while these modern networks typically recognize at least 1, 000 [UNK] categories of objects. the largest contest in object recognition is the imagenet large scale visual recognition challenge ( ilsvrc ) held each year. a dram... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 38 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
learning have brought the latest top - 5 error rate in this contest down to 3. 6 %, as shown in figure. 1. 12 deep learning has also had a dramatic impact on speech recognition. after improving throughout the 1990s, the error rates for speech recognition stagnated starting in about 2000. the introduction of deep learnin... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 38 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 1. introduction 1950 1985 2000 2015 101 102 103 104 connections per neuron 1 2 3 4 5 6 7 8 9 10 fruit fly mouse cat human figure 1. 10 : initially, the number of connections between neurons in artificial neural networks was limited by hardware capabilities. today, the number of connections between neurons is most... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 39 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##on 2009a, ) 5. unsupervised convolutional network (, ) jarrett et al. 2009 6. gpu - accelerated multilayer perceptron (, ) ciresan et al. 2010 7. distributed autoencoder (, ) le et al. 2012 8. multi - gpu convolutional network (, ) krizhevsky et al. 2012 9. cots hpc unsupervised convolutional network (, ) coates et a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 39 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
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