Deep Learning with TensorFlow 2 and Keras - Second Edition

Spis treści książki

• Preface
  • Mission
  • Machine learning, artificial intelligence, and the deep learning Cambrian explosion
  • Who this book is for
  • What this book covers
  • What you need for this book
    • Downloading the example code
    • Download the color images
    • Conventions
  • Get in touch
    • Reviews
  • References
• Neural Network Foundations with TensorFlow 2.0
  • What is TensorFlow (TF)?
  • What is Keras?
  • What are the most important changes in TensorFlow 2.0?
  • Introduction to neural networks
  • Perceptron
    • A first example of TensorFlow 2.0 code
  • Multi-layer perceptron our first example of a network
    • Problems in training the perceptron and their solutions
    • Activation function sigmoid
    • Activation function tanh
    • Activation function ReLU
    • Two additional activation functions ELU and LeakyReLU
    • Activation functions
    • In short what are neural networks after all?
  • A real example recognizing handwritten digits
    • One-hot encoding (OHE)
    • Defining a simple neural network in TensorFlow 2.0
    • Running a simple TensorFlow 2.0 net and establishing a baseline
    • Improving the simple net in TensorFlow 2.0 with hidden layers
    • Further improving the simple net in TensorFlow with Dropout
    • Testing different optimizers in TensorFlow 2.0
    • Increasing the number of epochs
    • Controlling the optimizer learning rate
    • Increasing the number of internal hidden neurons
    • Increasing the size of batch computation
    • Summarizing experiments run for recognizing handwritten charts
  • Regularization
    • Adopting regularization to avoid overfitting
    • Understanding BatchNormalization
  • Playing with Google Colab CPUs, GPUs, and TPUs
  • Sentiment analysis
  • Hyperparameter tuning and AutoML
  • Predicting output
  • A practical overview of backpropagation
  • What have we learned so far?
  • Towards a deep learning approach
  • References
• TensorFlow 1.x and 2.x
  • Understanding TensorFlow 1.x
    • TensorFlow 1.x computational graph program structure
      • Computational graphs
    • Working with constants, variables, and placeholders
    • Examples of operations
      • Constants
      • Sequences
      • Random tensors
      • Variables
    • An example of TensorFlow 1.x in TensorFlow 2.x
  • Understanding TensorFlow 2.x
    • Eager execution
    • AutoGraph
    • Keras APIs three programming models
      • Sequential API
      • Functional API
      • Model subclassing
    • Callbacks
    • Saving a model and weights
    • Training from tf.data.datasets
    • tf.keras or Estimators?
    • Ragged tensors
    • Custom training
    • Distributed training in TensorFlow 2.x
      • Multiple GPUs
      • MultiWorkerMirroredStrategy
      • TPUStrategy
      • ParameterServerStrategy
    • Changes in namespaces
    • Converting from 1.x to 2.x
    • Using TensorFlow 2.x effectively
  • The TensorFlow 2.x ecosystem
    • Language bindings
  • Keras or tf.keras?
  • Summary
• Regression
  • What is regression?
  • Prediction using linear regression
    • Simple linear regression
    • Multiple linear regression
    • Multivariate linear regression
  • TensorFlow Estimators
    • Feature columns
    • Input functions
    • MNIST using TensorFlow Estimator API
  • Predicting house price using linear regression
  • Classification tasks and decision boundaries
    • Logistic regression
    • Logistic regression on the MNIST dataset
  • Summary
  • References
• Convolutional Neural Networks
  • Deep Convolutional Neural Network (DCNN)
    • Local receptive fields
    • Shared weights and bias
    • A mathematical example
    • ConvNets in TensorFlow 2.x
    • Pooling layers
      • Max pooling
      • Average pooling
      • ConvNets summary
  • An example of DCNN LeNet
    • LeNet code in TensorFlow 2.0
    • Understanding the power of deep learning
  • Recognizing CIFAR-10 images with deep learning
    • Improving the CIFAR-10 performance with a deeper network
    • Improving the CIFAR-10 performance with data augmentation
    • Predicting with CIFAR-10
  • Very deep convolutional networks for large-scale image recognition
    • Recognizing cats with a VGG16 Net
    • Utilizing tf.keras built-in VGG16 Net module
    • Recycling prebuilt deep learning models for extracting features
  • Summary
  • References
• Advanced Convolutional Neural Networks
  • Computer vision
    • Composing CNNs for complex tasks
      • Classification and localization
      • Semantic segmentation
      • Object detection
      • Instance segmentation
    • Classifying Fashion-MNIST with a tf.keras - estimator model
    • Run Fashion-MNIST the tf.keras - estimator model on GPUs
    • Deep Inception-v3 Net used for transfer learning
    • Transfer learning for classifying horses and humans
    • Application Zoos with tf.keras and TensorFlow Hub
      • Keras applications
      • TensorFlow Hub
    • Other CNN architectures
      • AlexNet
      • Residual networks
      • HighwayNets and DenseNets
      • Xception
    • Answering questions about images (VQA)
    • Style transfer
      • Content distance
      • Style distance
    • Creating a DeepDream network
    • Inspecting what a network has learned
  • Video
    • Classifying videos with pretrained nets in six different ways
  • Textual documents
    • Using a CNN for sentiment analysis
  • Audio and music
    • Dilated ConvNets, WaveNet, and NSynth
  • A summary of convolution operations
    • Basic convolutional neural networks (CNN or ConvNet)
    • Dilated convolution
      • Transposed convolution
    • Separable convolution
    • Depthwise convolution
    • Depthwise separable convolution
  • Capsule networks
    • So what is the problem with CNNs?
    • So what is new with Capsule networks?
  • Summary
  • References
• Generative Adversarial Networks
  • What is a GAN?
    • MNIST using GAN in TensorFlow
  • Deep convolutional GAN (DCGAN)
    • DCGAN for MNIST digits
  • Some interesting GAN architectures
    • SRGAN
    • CycleGAN
    • InfoGAN
  • Cool applications of GANs
  • CycleGAN in TensorFlow 2.0
  • Summary
  • References
• Word Embeddings
  • Word embedding origins and fundamentals
  • Distributed representations
  • Static embeddings
    • Word2Vec
    • GloVe
  • Creating your own embedding using gensim
  • Exploring the embedding space with gensim
  • Using word embeddings for spam detection
    • Getting the data
    • Making the data ready for use
    • Building the embedding matrix
    • Define the spam classifier
    • Train and evaluate the model
    • Running the spam detector
  • Neural embeddings not just for words
    • Item2Vec
    • node2vec
  • Character and subword embeddings
  • Dynamic embeddings
  • Sentence and paragraph embeddings
  • Language model-based embeddings
    • Using BERT as a feature extractor
    • Fine-tuning BERT
    • Classifying with BERT command line
    • Using BERT as part of your own network
  • Summary
  • References
• Recurrent Neural Networks
  • The basic RNN cell
    • Backpropagation through time (BPTT)
    • Vanishing and exploding gradients
  • RNN cell variants
    • Long short-term memory (LSTM)
    • Gated recurrent unit (GRU)
    • Peephole LSTM
  • RNN variants
    • Bidirectional RNNs
    • Stateful RNNs
  • RNN topologies
    • Example One-to-Many learning to generate text
    • Example Many-to-One Sentiment Analysis
    • Example Many-to-Many POS tagging
  • Encoder-Decoder architecture seq2seq
    • Example seq2seq without attention for machine translation
  • Attention mechanism
    • Example seq2seq with attention for machine translation
  • Transformer architecture
  • Summary
  • References
• Autoencoders
  • Introduction to autoencoders
  • Vanilla autoencoders
    • TensorFlow Keras layers defining custom layers
    • Reconstructing handwritten digits using an autoencoder
  • Sparse autoencoder
  • Denoising autoencoders
    • Clearing images using a Denoising autoencoder
  • Stacked autoencoder
    • Convolutional autoencoder for removing noise from images
    • Keras autoencoder example sentence vectors
  • Summary
  • References
• Unsupervised Learning
  • Principal component analysis
    • PCA on the MNIST dataset
    • TensorFlow Embedding API
    • K-means clustering
    • K-means in TensorFlow 2.0
    • Variations in k-means
  • Self-organizing maps
    • Colour mapping using SOM
  • Restricted Boltzmann machines
    • Reconstructing images using RBM
    • Deep belief networks
  • Variational Autoencoders
  • Summary
  • References
• Reinforcement Learning
  • Introduction
    • RL lingo
    • Deep reinforcement learning algorithms
    • Reinforcement success in recent years
  • Introduction to OpenAI Gym
    • Random agent playing Breakout
  • Deep Q-Networks
    • DQN for CartPole
    • DQN to play a game of Atari
    • DQN variants
      • Double DQN
      • Dueling DQN
      • Rainbow
  • Deep deterministic policy gradient
  • Summary
  • References
• TensorFlow and Cloud
  • Deep learning on cloud
    • Microsoft Azure
    • Amazon Web Services (AWS)
    • Google Cloud Platform (GCP)
    • IBM Cloud
  • Virtual machines on cloud
    • EC2 on Amazon
    • Compute Instance on GCP
    • Virtual machine on Microsoft Azure
  • Jupyter Notebooks on cloud
    • SageMaker
    • Google Colaboratory
    • Microsoft Azure Notebooks
  • TensorFlow Extended for production
    • TFX Pipelines
    • TFX pipeline components
    • TFX libraries
  • TensorFlow Enterprise
  • Summary
  • References
• TensorFlow for Mobile and IoT and TensorFlow.js
  • TensorFlow Mobile
  • TensorFlow Lite
    • Quantization
    • FlatBuffers
    • Mobile converter
    • Mobile optimized interpreter
    • Supported platforms
    • Architecture
    • Using TensorFlow Lite
    • A generic example of application
    • Using GPUs and accelerators
    • An example of application
  • Pretrained models in TensorFlow Lite
    • Image classification
    • Object detection
    • Pose estimation
    • Smart reply
    • Segmentation
    • Style transfer
    • Text classification
    • Question and answering
    • A note about using mobile GPUs
  • An overview of federated learning at the edge
    • TensorFlow FL APIs
  • TensorFlow.js
    • Vanilla TensorFlow.js
    • Converting models
    • Pretrained models
    • Node.js
  • Summary
  • References
• An introduction to AutoML
  • What is AutoML?
  • Achieving AutoML
  • Automatic data preparation
  • Automatic feature engineering
  • Automatic model generation
  • AutoKeras
  • Google Cloud AutoML
    • Using Cloud AutoML Tables solution
    • Using Cloud AutoML Vision solution
    • Using Cloud AutoML Text Classification solution
    • Using Cloud AutoML Translation solution
    • Using Cloud AutoML Video Intelligence Classification solution
    • Cost
  • Bringing Google AutoML to Kaggle
  • Summary
  • References
• The Math Behind Deep Learning
  • History
  • Some mathematical tools
    • Derivatives and gradients everywhere
    • Gradient descent
    • Chain rule
    • A few differentiation rules
    • Matrix operations
  • Activation functions
    • Derivative of the sigmoid
    • Derivative of tanh
    • Derivative of ReLU
  • Backpropagation
    • Forward step
    • Backstep
      • Case 1 From hidden layer to output layer
      • Case 2 From hidden layer to hidden layer
    • Limit of backpropagation
    • Cross entropy and its derivative
    • Batch gradient descent, stochastic gradient descent, and mini-batch
      • Batch Gradient Descent (BGD)
      • Stochastic Gradient Descent (SGD)
      • Mini-Batch Gradient Descent (MBGD)
  • Thinking about backpropagation and convnets
  • Thinking about backpropagation and RNNs
  • A note on TensorFlow and automatic differentiation
  • Summary
  • References
• Tensor Processing Unit
  • C/G/T processing units
    • CPUs and GPUs
    • TPUs
  • Three generations of TPUs and Edge TPU
    • First-generation TPU
    • Second-generation TPU
    • Third-generation TPU
    • Edge TPU
  • TPU performance
  • How to use TPUs with Colab
    • Checking whether TPUs are available
    • Loading data with tf.data
    • Building a model and loading it into the TPU
  • Using pretrained TPU models
  • Using TensorFlow 2.1 and nightly build
  • Summary
  • References
• Other Books You May Enjoy
• Index

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