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Deep learning with PyTorch : a practical approach to building neural network models using PyTorch

Deep learning with PyTorch : a practical approach to building neural network models using PyTorch (Loan 12 times)

Material type
단행본
Personal Author
Subramanian, Vishnu.
Title Statement
Deep learning with PyTorch : a practical approach to building neural network models using PyTorch / Vishnu Subramanian.
Publication, Distribution, etc
Birmingham :   Packt Publishing,   c2018.  
Physical Medium
vii, 243 p. : ill. ; 24 cm.
ISBN
9781788624336 1788624335
요약
This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
General Note
Includes index.  
Subject Added Entry-Topical Term
Neural networks (Computer science). Machine learning. Python (Computer program language).
000 00000cam u2200205 a 4500
001 000045948501
005 20180726155837
008 180726s2018 enka 001 0 eng d
020 ▼a 9781788624336
020 ▼a 1788624335
035 ▼a (KERIS)BIB000014803139
040 ▼a 211046 ▼c 211046 ▼d 211009
082 0 4 ▼a 006.32 ▼2 23
084 ▼a 006.32 ▼2 DDCK
090 ▼a 006.32 ▼b S941d
100 1 ▼a Subramanian, Vishnu.
245 1 0 ▼a Deep learning with PyTorch : ▼b a practical approach to building neural network models using PyTorch / ▼c Vishnu Subramanian.
260 ▼a Birmingham : ▼b Packt Publishing, ▼c c2018.
300 ▼a vii, 243 p. : ▼b ill. ; ▼c 24 cm.
500 ▼a Includes index.
520 ▼a This book provides the intuition behind the state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and encoder-decoder without diving deep into the math of it. It shows how you can implement and use various architectures to solve problems in the area of image classification, language translation and NLP using PyTorch.
650 0 ▼a Neural networks (Computer science).
650 0 ▼a Machine learning.
650 0 ▼a Python (Computer program language).
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.32 S941d Accession No. 121245403 Availability Available Due Date Make a Reservation Service B M

Contents information

Author Introduction

비슈누 수브라마니안(지은이)

다수의 빅데이터 분석 프로젝트(인공지능, 머신 러닝 및 딥러닝)에서 프로젝트 리딩, 설계 및 구현 역할을 담당했다. 머신 러닝, 딥러닝, 분산 환경 머신 러닝 및 시각화에 전문성을 갖 고 있다. 유통, 금융 및 여행 분야에서 경험을 쌓았다. 비지니스, 인공지능 그리고 엔지니어 팀 간의 이해와 소통을 돕고 조정하는 데 능숙하다.

Information Provided By: : Aladin

Table of Contents

CONTENTS
Preface = 1
Chapter 1 Getting Started with Deep Learning Using PyTorch = 7
 Artificial intelligence = 8
  The history of AI = 8
 Machine learning = 9
  Examples of machine learning in real life = 10
 Deep learning = 10
  Applications of deep learning = 11
  Hype associated with deep learning = 13
  The history of deep learning = 14
  Why now? = 14
  Hardware availability = 14
  Data and algorithms = 16
  Deep learning frameworks = 16
 Summary = 18
Chapter 2 Building Blocks of Neural Networks = 19
 Installing PyTorch = 19
 Our first neural network = 20
  Data preparation = 21
  Creating data for our neural network = 31
  Loading data = 35
 Summary = 37
Chapter 3 Diving Deep into Neural Networks = 39
 Deep dive into the building blocks of neural networks = 39
  Layers - fundamental blocks of neural networks = 41
  Non-linear activations = 43
  PyTorch non-linear activations = 47
  Image classification using deep learning = 52
 Summary = 64
Chapter 4 Fundamentals of Machine Learning = 65
 Three kinds of machine learning problems = 65
  Supervised learning = 66
  Unsupervised learning = 66
  Reinforcement learning = 67
 Machine learning glossary = 67
 Evaluating machine learning models = 68
  Training, validation, and test split = 69
 Data preprocessing and feature engineering = 73
  Vectorization = 73
  Value normalization = 73
  Handling missing values = 74
  Feature engineering = 74
 Overfitting and underfitting = 75
  Getting more data = 76
  Reducing the size of the network = 76
  Applying weight regularization = 77
  Dropout = 78
  Underfitting = 80
 Workflow of a machine learning project = 80
  Problem definition and dataset creation = 80
  Measure of success = 81
  Evaluation protocol = 82
  Prepare your data = 82
  Baseline model = 82
  Large model enough to overfit = 83
  Applying regularization = 84
  Learning rate picking strategies = 85
 Summary = 86
Chapter 5 Deep Learning for Computer Vision = 87
 Introduction to neural networks = 88
  MNIST - getting data = 89
 Building a CNN model from scratch = 91
  Conv2d = 93
  Pooling = 97
  Nonlinear activation - ReLU = 99
  View = 99
  Training the model = 101
  Classifying dogs and cats - CNN from scratch = 104
  Classifying dogs and cats using transfer learning = 106
 Creating and exploring a VGG16 model = 108
  Freezing the layers = 110
  Fine-tuning VGG16 = 110
  Training the VGG16 model = 110
 Calculating pre-convoluted features = 113
 Understanding what a CNN model learns = 116
  Visualizing outputs from intermediate layers = 116
 Visualizing weights of the CNN layer = 121
 Summary = 121
Chapter 6 Deep Learning with Sequence Data and Text = 123
 Working with text data = 124
  Tokenization = 125
  Vectorization = 128
 Training word embeddingby building a sentiment classifier = 132
  Downloading IMDB data and performing text tokenization = 133
  Building vocabulary = 135
  Generate batches of vectors = 136
  Creating a network model with embedding = 137
  Training the model = 138
 Using pretrained word embeddings = 140
  Downloading the embeddings = 140
  Loading the embeddings in the model = 141
  Freeze the embedding layer weights = 142
 Recursive neural networks = 143
  Understanding how RNN works with an example = 144
 LSTM = 147
  Long-term dependency = 147
  LSTM networks = 147
 Convolutional network on sequence data = 153
  Understanding one-dimensional convolution for sequence data = 154
 Summary = 156
Chapter 7 Generative Networks = 157
 Neural style transfer = 158
  Loading the data = 160
  Creating the VGG model = 162
  Content loss = 163
  Style loss = 163
  Extracting the losses = 166
  Creating loss function for each layers = 169
  Creating the optimizer = 169
  Training = 170
 Generative adversarial networks = 171
 Deep convolutional GAN = 173
  Defining the generator network = 173
  Defining the discriminator network = 178
  Defining loss and optimizer = 179
  Training the discriminator = 180
  Training the generator network = 181
  Training the complete network = 181
  Inspecting the generated images = 183
 Language modeling = 184
  Preparing the data = 185
  Generating the batches = 187
  Defining a model based on LSTM = 188
  Defining the train and evaluate functions = 190
  Training the model = 193
 Summary = 195
Chapter 8 Modern Network Architectures = 197
 Modern network architectures = 197
  ResNet = 198
  Inception = 206
 Densely connected convolutional networks - DenseNet = 213
  DenseBlock = 214
  DenseLayer = 215
 Model ensembling = 219
  Creating models = 221
  Extracting the image features = 221
  Creating a custom dataset along with data loaders = 223
  Creating an ensembling model = 224
  Training and validating the model = 224
 Encoder-decoder architecture = 226
  Encoder = 228
  Decoder = 228
 Summary = 228
Chapter 9 What Next? = 229
 What next? = 229
 Overview = 229
 Interesting ideas to explore = 230
  Object detection = 231
  Image segmentation = 232
  OpenNMT in PyTorch = 233
  Alien NLP = 233
  fast.ai - making neural nets uncool again = 233
  Open Neural Network Exchange = 234
 How to keep yourself updated = 234
 Summary = 234
Other Books You May Enjoy = 235
Index = 239

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