这是《fundamental of deep learning》的私人中文版 😊。

原书信息

Author: Nicholas Locascio, Nikhil Buduma

ISBN-10: 1491925612

Year: 2017

Pages: 304

目录

• Ch1. The Neural Network
  • Building Intelligent Machines
  • The Limits of Traditional Computer Programs
  • The Mechanics of Machine Learning
  • The Neuron
  • Expressing Linear Perceptrons as Neurons
  • Feed-Forward Neural Networks
  • Linear Neurons and Their Limitations
  • Sigmoid, Tanh and ReLU Neurons
  • Softmax Output Layers
  • Looking Forward
• Ch2. Training Feed-Forward Neural Networks
  • The Fast-Food Problem
  • Gradient Descent
  • The Delta Rule and Learning Rates
  • Gradient Descent with Sigmoidal Neurons
  • The Backpropagation Algorithm
  • Stochastic and Minibatch Gradient Descent
  • Test Sets, Validation Sets, and Overfitting
  • Preventing Overfitting in Deep Neural Networks
  • Summary
• Ch3. Implementing Neural Networks in TensorFlow
  • What Is TensorFlow?
  • How Does TensorFlow Compare to Alternatives?
  • Installing TensorFlow
  • Creating and Manipulating TensorFlow Variables
  • TensorFlow Operations
  • Placeholder Tensors
  • Sessions in TensorFlow
  • Navigating Variable Scopes and Sharing Variables
  • Managing Models over the CPU and GPU
  • Specifying the Logistic Regression Model in TensorFlow
  • Logging and Training the Logistic Regression Model
  • Leveraging TensorBoard to Visualize Computation Graphs and Learning
  • Building a Multilayer Model for MNIST in TensorFlow
  • Summary
• Ch4. Beyond Gradient Descent
  • The Challenges with Gradient Descent
  • Local Minima in the Error Surfaces of Deep Networks
  • Model Identifiability
  • How Pesky Are Spurious Local Minima in Deep Networks?
  • Flat Regions in the Error Surface
  • When the Gradient Points in the Wrong Direction
  • Momentum-Based Optimization
  • A Brief View of Second-Order Methods
  • Learning Rate Adaptation
  • The Philosophy Behind Optimizer Selection
  • Summary
• Ch5. Convolutional Neural Networks
  • Neurons in Human Vision
  • The Shortcomings of Feature Selection
  • Vanilla Deep Neural Networks Don’t Scale
  • Filters and Feature Maps
  • Full Description of the Convolutional Layer
  • Max Pooling
  • Full Architectural Description of Convolution Networks
  • Closing the Loop on MNIST with Convolutional Networks
  • Image Preprocessing Pipelines Enable More Robust Models
  • Accelerating Training with Batch Normalization
  • Building a Convolutional Network for CIFAR-10
  • Visualizing Learning in Convolutional Networks
  • Leveraging Convolutional Filters to Replicate Artistic Styles
  • Learning Convolutional Filters for Other Problem Domains
  • Summary
• Ch6. Embedding and Representation Learning
  • Learning Lower-Dimensional Representations
  • Principal Component Analysis
  • Motivating the Autoencoder Architecture
  • Implementing an Autoencoder in TensorFlow
  • Denoising to Force Robust Representations
  • Sparsity in Autoencoders
  • When Context Is More Informative than the Input Vector
  • The Word2Vec Framework
  • Implementing the Skip-Gram Architecture
  • Summary
• Ch7. Models for Sequence Analysis
  • Analyzing Variable-Length Inputs
  • Tackling seq2seq with Neural N-Grams
  • Implementing a Part-of-Speech Tagger
  • Dependency Parsing and SyntaxNet
  • Beam Search and Global Normalization
  • A Case for Stateful Deep Learning Models
  • Recurrent Neural Networks
  • The Challenges with Vanishing Gradients
  • Long Short-Term Memory (LSTM) Units
  • TensorFlow Primitives for RNN Models
  • Implementing a Sentiment Analysis Model
  • Solving seq2seq Tasks with Recurrent Neural Networks
  • Augmenting Recurrent Networks with Attention
  • Dissecting a Neural Translation Network
  • Summary
• Ch8. Memory Augmented Neural Networks
  • Neural Turing Machines
  • Attention-Based Memory Access
  • NTM Memory Addressing Mechanisms
  • Differentiable Neural Computers
  • Interference-Free Writing in DNCs
  • DNC Memory Reuse
  • Temporal Linking of DNC Writes
  • Understanding the DNC Read Head
  • The DNC Controller Network
  • Visualizing the DNC in Action
  • Implementing the DNC in TensorFlow
  • Teaching a DNC to Read and Comprehend
  • Summary
• Ch9. Deep Reinforcement Learning
  • Deep Reinforcement Learning Masters Atari Games
  • What Is Reinforcement Learning?
  • Markov Decision Processes (MDP)
  • Explore Versus Exploit
  • Policy Versus Value Learning
  • Pole-Cart with Policy Gradients
  • Q-Learning and Deep Q-Networks
  • Improving and Moving Beyond DQN
  • Summary