000 | 00000nam u2200205 a 4500 | |

001 | 000045935549 | |

005 | 20180315110141 | |

008 | 180314s2018 nyua 001 0 eng d | |

020 | ▼a 9781617294433 | |

035 | ▼a (KERIS)BIB000014702280 | |

040 | ▼a 211048 ▼c 211048 ▼d 211009 | |

082 | 0 4 | ▼a 006.31 ▼2 23 |

084 | ▼a 006.31 ▼2 DDCK | |

090 | ▼a 006.31 ▼b C547d | |

100 | 1 | ▼a Chollet, François. |

245 | 1 0 | ▼a Deep learning with python / ▼c François Chollet. |

260 | ▼a Shelter Island, NY : ▼b Manning, ▼c c2018. | |

300 | ▼a xxi, 361 p. : ▼b ill. ; ▼c 24 cm. | |

500 | ▼a Includes index. | |

650 | 0 | ▼a Python (Computer program language). |

650 | 0 | ▼a Machine learning. |

650 | 0 | ▼a Neural networks (Computer science). |

945 | ▼a KLPA |

### Holdings Information

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No. 1 | Location Main Library/Western Books/ | Call Number 006.31 C547d | Accession No. 111787697 | Availability Available | Due Date | Make a Reservation | Service |

No. 2 | Location Main Library/Western Books/ | Call Number 006.31 C547d | Accession No. 111810246 | Availability Available | Due Date | Make a Reservation | Service |

### Contents information

#### Author Introduction

#### 프랑소와 숄레(지은이)

캘리포니아 마운틴 뷰의 구글에서 딥러닝과 관련된 일을 한다. 케라스 딥러닝 라이브러리의 창시자이고 텐서플로 머신 러닝 프레임워크의 기여자다. 컴퓨터 비전과 형식 추론을 위한 머신 러닝 애플리케이션에 초점을 맞춰 딥러닝을 연구한다. 그의 논문은 CVPR(Computer Vision and Pattern Recognition), NIPS(Neural Information Processing Systems), ICLR(International Conference on Learning Representations) 등의 주요 콘퍼런스와 워크숍에서 소개되었다.

#### Table of Contents

Preface p. xiii Acknowledgments p. xv About this book p. xvi About the author p. xx About the cover p. xxi Part 1 Fundamentals of deep learning p. 1 1 What is deep learning? p. 3 1.1 Artificial intelligence, machine learning, and deep learning p. 4 Artificial intelligence p. 4 Machine learning p. 4 Learning representations from data p. 6 The "deep" in deep learning p. 8 Understanding how deep learning works, in three figures p. 9 What deep learning has achieved so far p. 11 Don''t believe the short-term hype p. 12 The promise of AI p. 13 1.2 Before deep learning: a brief history of machine learning p. 14 Probabilistic modeling p. 14 Early neural networks p. 14 Kernel methods p. 15 Decision trees, random forests, and gradient boosting machines p. 16 Back to neural networks p. 17 What makes deep learning different p. 17 The modern machine-learning landscape p. 18 1.3 Why deep learning? Why now? p. 20 Hardware p. 20 Data p. 21 Algorithms p. 21 A new wave of investment p. 22 The democratization of deep learning p. 23 Will it last? p. 23 2 Before we begin: the mathematical building blocks of neural networks p. 25 2.1 A first look at a neural network p. 27 2.2 Data representations for neural networks p. 31 Scalars (0D tensors) p. 31 Vectors (1D tensors) p. 31 Matrices (2D tensors) p. 31 3D tensors and higher-dimensional tensors p. 32 Key attributes p. 32 Manipulating tensors in Numpy p. 34 The notion of data batches p. 34 Real-world examples of data tensors p. 35 Vector data p. 35 Timeseries data or sequence data p. 35 Image data p. 36 Video data p. 37 2.3 The gears of neural networks: tensor operations p. 38 Element-wise operations p. 38 Broadcasting p. 39 Tensor dot p. 40 Tensor reshaping p. 42 Geometric interpretation of tensor operations p. 43 A geometric interpretation of deep learning p. 44 2.4 The engine of neural networks: gradient-based optimization p. 46 What''s a derivative? p. 47 Derivative of a tensor operation: the gradient p. 48 Stochastic gradient descent p. 48 Chaining derivatives: the Backpropagation algorithm p. 51 2.5 Looking back at our first example p. 53 2.6 Chapter summary p. 55 3 Getting started with neural networks p. 56 3.1 Anatomy of a neural network p. 58 Layers: the building blocks of deep learning p. 58 Models: networks of layers p. 59 Loss functions and optimizers: keys to configuring the learning process p. 60 3.2 Introduction to Keras p. 61 Keras, TensorFlow, Theano, and CNTK p. 62 Developing with Keras: a quick overview p. 62 3.3 Setting up a deep-learning workstation p. 65 Jupyter notebooks: the preferred way to run deep-learning experiments p. 65 Getting Keras running: two options p. 66 Running deep-learning jobs in the cloud: pros and cons p. 66 What is the best GPU for deep learning? p. 66 3.4 Classifying movie reviews: a binary classification example p. 68 The IMDB dataset p. 68 Preparing the data p. 69 Building your network p. 70 Validating your approach p. 73 Using a trained network to generate predictions on new data p. 76 Further experiments p. 77 Wrapping up p. 77 3.5 Classifying newswires: a multiclass classification example p. 78 The Reuters dataset p. 78 Preparing the data p. 79 Building your network p. 79 Validating your approach p. 80 Generating predictions on new data p. 83 A different way to handle the labels and the loss p. 83 The importance of having sufficiently large intermediate layers p. 83 Further experiments p. 84 Wrapping up p. 84 3.6 Predicting house prices: a regression example p. 85 The Boston Housing Price dataset p. 85 Preparing the data p. 86 Building your network p. 86 Validating your approach using K-fold validation p. 87 Wrapping up p. 91 3.7 Chapter summary p. 92 4 Fundamentals of machine learning p. 93 4.1 Four branches of machine learning p. 94 Supervised learning p. 94 Unsupervised learning p. 94 Self-supervised learning p. 94 Reinforcement learning p. 95 4.2 Evaluating machine-learning models p. 97 Training validation, and test sets p. 97 Things to keep in mind p. 100 4.3 Data preprocessing, feature engineering, and feature learning p. 101 Data preprocessing for neural networks p. 101 Feature engineering p. 102 4.4 Overfitting and underfitting p. 104 Reducing the network''s size p. 104 Adding weight regularization p. 107 Adding dropout p. 109 4.5 The universal workflow of machine learning p. 111 Defining the problem and assembling a dataset p. 111 Choosing a measure of success p. 112 Deciding on an evaluation protocol p. 112 Preparing your data p. 112 Developing a model that does better than a baseline p. 113 Scaling up: developing a model that overfits p. 114 Regularizing your model and luning your hyperparameters p. 114 4.6 Chapter summary p. 116 Part 2 Deep Learning in Practice p. 117 5 Deep learning for computer vision p. 119 5.1 Introduction to convnets p. 120 The convolution operation p. 122 The max-pooling operation p. 127 5.2 Training a convnet from scratch on a small dataset p. 130 The relevance of deep learning for small-data problems p. 130 Downloading the data p. 131 Building your network p. 133 Data preprocessing p. 135 Using data augmentation p. 138 5.3 Using a pretrained convnet p. 143 Feature extraction p. 143 Fine-luning p. 152 Wrapping up p. 159 5.4 Visualizing what convnets learn p. 160 Visualizing intermediate activations p. 160 Visualizing convnet filters p. 167 Visualizing heatmaps of class activation p. 172 5.5 Chapter summary p. 177 6 Deep learning for text and sequences p. 178 6.1 Working with text data p. 180 One-hot encoding of words and characters p. 181 Using word embeddings p. 184 Putting it all together: from raw text to word embeddings p. 188 Wrapping up p. 195 6.2 Understanding recurrent neural networks p. 196 A recurrent layer in Keras p. 198 Understanding the LSTM and GRU layers p. 202 A concrete LSTM example in Keras p. 204 Wrapping up p. 206 6.3 Advanced use of recurrent neural networks p. 207 A temperature-forecasting problem p. 207 Preparing the data p. 210 A common-sense, non-machine-learning baseline p. 212 A basic machine-learning approach p. 213 A first recurrent baseline p. 215 Using recurrent dropout to fight overfitting p. 216 Stacking recurrent layers p. 217 Using bidirectional RNNs p. 219 Going even further p. 222 Wrapping up p. 223 6.4 Sequence processing with convnets p. 225 Understanding 1D convolution for sequence data p. 225 1D pooling for sequence data p. 226 Implementing a 1D convnet p. 226 Combining CNNs and RNNs to process long sequences p. 228 Wrapping up p. 231 6.5 Chapter summary p. 232 7 Advanced deep-learning best practices p. 233 7.1 Going beyond the Sequential model: the Keras functional API p. 234 Introduction to the functional API p. 236 Multi-input models p. 238 Multi-output models p. 240 Directed acyclic graphs of layers p. 242 Layer weight sharing p. 246 Models as layers p. 247 Wrapping up p. 248 7.2 Inspecting and monitoring deep-learning models using Keras callbacks and TensorBoard p. 249 Using callbacks to act on a model during training p. 249 Introduction to TensorBoard: the TensorFlow visualization framework p. 252 Wrapping up p. 259 7.3 Getting the most out of your models p. 260 Advanced architecture patterns p. 260 Hyperparameter optimization p. 263 Model ensembling p. 264 Wrapping up p. 266 7.4 Chapter summary p. 268 8 Generative deep learning p. 269 8.1 Text generation with LSTM p. 271 A brief history of generative recurrent networks p. 271 How do you generate sequence data? p. 272 The importance of the sampling strategy p. 272 Implementing character-level LSTM text generation p. 274 Wrapping up p. 279 8.2 DeepDream p. 280 Implementing DeepDream in Keras p. 281 Wrapping up p. 286 8.3 Neural style transfer p. 287 The content loss p. 288 The style loss p. 288 Neural style transfer in Keras p. 289 Wrapping up p. 295 8.4 Generating images with variational autoencoders p. 296 Sampling from latent spaces of images p. 296 Concept vectors for image editing p. 297 Variational autoencoders p. 298 Wrapping up p. 304 8.5 Introduction to generative adversarial networks p. 305 A schematic GAN implementation p. 307 A bag of tricks p. 307 The generator p. 308 The discriminator p. 309 The adversarial network p. 310 How to train your DCGAN p. 310 Wrapping up p. 312 8.6 Chapter summary p. 313 9 Conclusions p. 314 9.1 Key concepts in review p. 315 Various approaches to AI p. 315 What makes deep learning special within the field of machine learning p. 315 How to think about deep learning p. 316 Key enabling technologies p. 317 The universal machine-learning workflow p. 318 Key network architectures p. 319 The space of possibilities p. 322 9.2 The limitations of deep learning p. 325 The risk of anthropomorphizing machine-learning models p. 325 Local generalization vs. extreme generalization p. 327 Wrapping up p. 329 9.3 The future of deep learning p. 330 Models as programs p. 330 Beyond backpropagation and differentiable layers p. 332 Automated machine learning p. 332 Lifelong learning and modular subroutine reuse p. 333 The long-term vision p. 335 9.4 Staying up to date in a fast-moving field p. 337 Practice on real-world problems using Kaggle p. 337 Read about the latest developments on arXiv p. 337 Explore the Kerns ecosystem p. 338 9.5 Final words p. 339 Appendix A Installing Keras and its dependencies on Ubuntu p. 340 Appendix B Running Jupyter notebooks on an EC2 GPU instance p. 345 Index p. 353