更新时间：2021-08-27 19:19:02
封面
Deep Learning with TensorFlow - Second Edition
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Chapter 1. Getting Started with Deep Learning
A soft introduction to machine learning
Artificial neural networks
How does an ANN learn?
Neural network architectures
Deep learning frameworks
Summary
Chapter 2. A First Look at TensorFlow
A general overview of TensorFlow
What's new from TensorFlow v1.6 forwards?
Installing and configuring TensorFlow
TensorFlow computational graph
TensorFlow code structure
Data model in TensorFlow
Visualizing computations through TensorBoard
Linear regression and beyond
Chapter 3. Feed-Forward Neural Networks with TensorFlow
Feed-forward neural networks (FFNNs)
Implementing a feed-forward neural network
Implementing a multilayer perceptron (MLP)
Tuning hyperparameters and advanced FFNNs
Chapter 4. Convolutional Neural Networks
Main concepts of CNNs
CNNs in action
LeNet5
Implementing a LeNet-5 step by step
Dataset preparation
Fine-tuning implementation
Inception-v3
Emotion recognition with CNNs
Chapter 5. Optimizing TensorFlow Autoencoders
How does an autoencoder work?
Implementing autoencoders with TensorFlow
Improving autoencoder robustness
Fraud analytics with autoencoders
Chapter 6. Recurrent Neural Networks
Working principles of RNNs
RNN and the gradient vanishing-exploding problem
Implementing an RNN for spam prediction
Developing a predictive model for time series data
An LSTM predictive model for sentiment analysis
Human activity recognition using LSTM model
Chapter 7. Heterogeneous and Distributed Computing
GPGPU computing
The TensorFlow GPU setup
Distributed computing
The distributed TensorFlow setup
Chapter 8. Advanced TensorFlow Programming
tf.estimator
TFLearn
PrettyTensor
Keras
Chapter 9. Recommendation Systems Using Factorization Machines
Recommendation systems
Movie recommendation using collaborative filtering
Factorization machines for recommendation systems
Improved factorization machines
Chapter 10. Reinforcement Learning
The RL problem
OpenAI Gym
The Q-Learning algorithm
Deep Q-learning
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