更新时间：2021-06-24 14:00:47
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Hands-On Neural Networks
Dedication
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Preface
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What this book covers
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Section 1: Getting Started
Getting Started with Supervised Learning
History of AI
An overview of machine learning
Supervised learning
Unsupervised learning
Semi-supervised learning
Reinforcement learning
Environment setup
Understanding virtual environments
Anaconda
Docker
Supervised learning in practice with Python
Data cleaning
Feature engineering
How deep learning performs feature engineering
Feature scaling
Feature engineering in Keras
Supervised learning algorithms
Metrics
Regression metrics
Classification metrics
Evaluating the model
TensorBoard
Summary
Neural Network Fundamentals
The perceptron
Implementing a perceptron
Keras
Implementing perceptron in Keras
Feedforward neural networks
Introducing backpropagation
Activation functions
Sigmoid
Softmax
Tanh
ReLU
Keras implementation
The chain rule
The XOR problem
FFNN in Python from scratch
FFNN Keras implementation
TensorBoard on the XOR problem
Section 2: Deep Learning Applications
Convolutional Neural Networks for Image Processing
Understanding CNNs
Input data
Convolutional layers
Pooling layers
Stride
Max pooling
Zero padding
Dropout layers
Normalization layers
Output layers
CNNs in Keras
Loading the data
Creating the model
Network configuration
Keras for expression recognition
Optimizing the network
Exploiting Text Embedding
Machine learning for NLP
Rule-based methods
Understanding word embeddings
Applications of words embeddings
Word2vec
Word embedding in Keras
Pre-trained network
GloVe
Global matrix factorization
Using the GloVe model
Text classification with GloVe
Working with RNNs
Understanding RNNs
