目录 Preface Chapter 1: Getting Started with Deep Learning A soft introduction to machine learning Supervised learning Unbalanced data Unsupervised learning Reinforcement learning What is deep learning? Artificial neural networks The biological neurons The artificial neuron How does an ANN learn? ANNs and the backpropagation algorithm Weight optimization Stochastic gradient descent Neural network architectures Deep Neural Networks (DNNs) Multilayer perceptron Deep Belief Networks (DBNs) Convolutional Neural Networks (CNNs) AutoEncoders Recurrent Neural Networks (RNNs) Emergent architectures Deep learning frameworks Summary Chapter 2: A First Look at TensorFlow A general overview of TensorFlow What''s new in TensorFlow vl.6? Nvidia GPU support optimized Introducing TensorFlow Lite Eager execution Optimized Accelerated Linear Algebra (XLA) Installing and configuring TensorFlow TensorFlow computational graph TensorFlow code structure Eager execution with TensorFIow Data model in TensorFlow Tensor Rank and shape Data type Variables Fetches Feeds and placeholders Visualizing computations through TensorBoard How does TensorBoard work? Linear regression and beyond Linear regression revisited for a real dataset Summary Chapter 3: Feed-Forward Neural Networks with TensorFIow Feed-forward neural networks (FFNNs) Feed-forward and backpropagation Weights and biases Activation functions Using sigmoid Using tanh Using ReLU Using softmax Implementing a feed-forward neural network Exploring the MNIST dataset Softmax classifier Implementing a multilayer perceptron (MLP) Training an MLP Using MLPs Dataset description Preprocessing A TensorFIow implementation of MLP for client-subscription assessment Chapter 4: Convolutional Neural Networks Chapter 5: Optimizing TensorFIow Autoencoders Chapter 6: Recurrent Neural Networks Chapter 7: Heterogeneous and Distributed Computing Chapter 8: Advanced TensorFIow Programming Chapter 9: Recommendation Systems Using Factorization Machines Chapter 10: Reinforcement Learning Other Books You May Enjoy Index
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