Deep Learning with TensorFlow and Keras - 3rd Edition : Build and Deploy Supervised, Unsupervised, Deep, and Reinforcement Learning Models.

Bibliographic Details
Main Author: Kapoor, Amita
Other Authors: Gulli, Antonio, Pal, Sujit, Chollet, Fran cois
Format: e-Book
Language:English
Published: Birmingham : Packt Publishing, Limited, 2022.
Edition:3rd ed.
Subjects:
Electronic books.
Online Access:Full-text access
Table of Contents:
  • Cover
  • Copyright
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: Neural Network Foundations with TF
  • What is TensorFlow (TF)?
  • What is Keras?
  • Introduction to neural networks
  • Perceptron
  • Our first example of TensorFlow code
  • Multi-layer perceptron: our first example of a network
  • Problems in training the perceptron and solution
  • Activation function: sigmoid
  • Activation function: tanh
  • Activation function: ReLU
  • Two additional activation functions: ELU and Leaky ReLU
  • Activation functions
  • In short: what are neural networks after all?
  • A real example: recognizing handwritten digits
  • One hot-encoding (OHE)
  • Defining a simple neural net in TensorFlow
  • Running a simple TensorFlow net and establishing a baseline
  • Improving the simple net in TensorFlow with hidden layers
  • Further improving the simple net in TensorFlow with dropout
  • Testing different optimizers in TensorFlow
  • 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 to recognizing handwritten digits
  • Regularization
  • Adopting regularization to avoid overfitting
  • Understanding batch normalization
  • 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?
  • Toward a deep learning approach
  • Summary
  • References
  • Chapter 2: Regression and Classification
  • What is regression?
  • Prediction using linear regression
  • Simple linear regression
  • Multiple linear regression
  • Multivariate linear regression
  • Neural networks for linear regression
  • Simple linear regression using TensorFlow Keras.
  • Multiple and multivariate linear regression using the TensorFlow Keras API
  • Classification tasks and decision boundaries
  • Logistic regression
  • Logistic regression on the MNIST dataset
  • Summary
  • References
  • Chapter 3: Convolutional Neural Networks
  • Deep convolutional neural networks
  • Local receptive fields
  • Shared weights and bias
  • A mathematical example
  • ConvNets in TensorFlow
  • Pooling layers
  • Max pooling
  • Average pooling
  • ConvNets summary
  • An example of DCNN: LeNet
  • LeNet code in TF
  • 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 work
  • Utilizing the tf.Keras built-in VGG16 net module
  • Recycling pre-built deep learning models for extracting features
  • Deep Inception V3 for transfer learning
  • Other CNN architectures
  • AlexNet
  • Residual networks
  • HighwayNets and DenseNets
  • Xception
  • Style transfer
  • Content distance
  • Style distance
  • Summary
  • References
  • Chapter 4: Word Embeddings
  • Word embedding ‒ origins and fundamentals
  • Distributed representations
  • Static embeddings
  • Word2Vec
  • GloVe
  • Creating your own embeddings 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
  • Defining the spam classifier
  • Training and evaluating 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
  • Summary
  • References
  • Chapter 5: 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
  • Summary
  • References
  • Chapter 6: Transformers
  • Architecture
  • Key intuitions
  • Positional encoding
  • Attention
  • Self-attention
  • Multi-head (self-)attention
  • How to compute attention
  • Encoder-decoder architecture
  • Residual and normalization layers
  • An overview of the transformer architecture
  • Training
  • Transformers' architectures
  • Categories of transformers
  • Decoder or autoregressive
  • Encoder or autoencoding
  • Seq2seq
  • Multimodal
  • Retrieval
  • Attention
  • Full versus sparse
  • LSH attention
  • Local attention
  • Pretraining
  • Encoder pretraining
  • Decoder pretraining
  • Encoder-decoder pretraining
  • A taxonomy for pretraining tasks
  • An overview of popular and well-known models
  • BERT
  • GPT-2
  • GPT-3
  • Reformer
  • BigBird
  • Transformer-XL
  • XLNet
  • RoBERTa
  • ALBERT
  • StructBERT
  • T5 and MUM
  • ELECTRA
  • DeBERTa
  • The Evolved Transformer and MEENA
  • LaMDA
  • Switch Transformer
  • RETRO
  • Pathways and PaLM
  • Implementation
  • Transformer reference implementation: An example of translation
  • Hugging Face
  • Generating text
  • Autoselecting a model and autotokenization.
  • Named entity recognition
  • Summarization
  • Fine-tuning
  • TFHub
  • Evaluation
  • Quality
  • GLUE
  • SuperGLUE
  • SQuAD
  • RACE
  • NLP-progress
  • Size
  • Larger doesn't always mean better
  • Cost of serving
  • Optimization
  • Quantization
  • Weight pruning
  • Distillation
  • Common pitfalls: dos and don'ts
  • Dos
  • Don'ts
  • The future of transformers
  • Summary
  • Chapter 7: Unsupervised Learning
  • Principal component analysis
  • PCA on the MNIST dataset
  • TensorFlow Embedding API
  • K-means clustering
  • K-means in TensorFlow
  • Variations in k-means
  • Self-organizing maps
  • Colour mapping using a SOM
  • Restricted Boltzmann machines
  • Reconstructing images using an RBM
  • Deep belief networks
  • Summary
  • References
  • Chapter 8: 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
  • A TensorFlow Keras autoencoder example ‒ sentence vectors
  • Variational autoencoders
  • Summary
  • References
  • Chapter 9: Generative Models
  • 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
  • Flow-based models for data generation
  • Diffusion models for data generation
  • Summary
  • References
  • Chapter 10: Self-Supervised Learning
  • Previous work
  • Self-supervised learning
  • Self-prediction
  • Autoregressive generation
  • PixelRNN
  • Image GPT (IPT)
  • GPT-3
  • XLNet
  • WaveNet
  • WaveRNN
  • Masked generation
  • BERT
  • Stacked denoising autoencoder.
  • Context autoencoder
  • Colorization
  • Innate relationship prediction
  • Relative position
  • Solving jigsaw puzzles
  • Rotation
  • Hybrid self-prediction
  • VQ-VAE
  • Jukebox
  • DALL-E
  • VQ-GAN
  • Contrastive learning
  • Training objectives
  • Contrastive loss
  • Triplet loss
  • N-pair loss
  • Lifted structural loss
  • NCE loss
  • InfoNCE loss
  • Soft nearest neighbors loss
  • Instance transformation
  • SimCLR
  • Barlow Twins
  • BYOL
  • Feature clustering
  • DeepCluster
  • SwAV
  • InterCLR
  • Multiview coding
  • AMDIM
  • CMC
  • Multimodal models
  • CLIP
  • CodeSearchNet
  • Data2Vec
  • Pretext tasks
  • Summary
  • References
  • Chapter 11: Reinforcement Learning
  • An introduction to RL
  • RL lingo
  • Deep reinforcement learning algorithms
  • How does the agent choose its actions, especially when untrained?
  • How does the agent maintain a balance between exploration and exploitation?
  • How to deal with the highly correlated input state space
  • How to deal with the problem of moving targets
  • Reinforcement success in recent years
  • Simulation environments for RL
  • An introduction to OpenAI Gym
  • Random agent playing Breakout
  • Wrappers in Gym
  • 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
  • Chapter 12: Probabilistic TensorFlow
  • TensorFlow Probability
  • TensorFlow Probability distributions
  • Using TFP distributions
  • Coin Flip Example
  • Normal distribution
  • Bayesian networks
  • Handling uncertainty in predictions using TensorFlow Probability
  • Aleatory uncertainty
  • Epistemic uncertainty
  • Creating a synthetic dataset
  • Building a regression model using TensorFlow
  • Probabilistic neural networks for aleatory uncertainty
  • Accounting for the epistemic uncertainty
  • Summary
  • References.
  • Chapter 13: An Introduction to AutoML.

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