更新时间：2021-06-24 14:48:52
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Hands-On Deep Learning Architectures with Python
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Preface
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Section 1: The Elements of Deep Learning
Getting Started with Deep Learning
Artificial intelligence
Machine learning
Supervised learning
Regression
Classification
Unsupervised learning
Reinforcement learning
Deep learning
Applications of deep learning
Self-driving cars
Image translation
Machine translation
Encoder-decoder structure
Chatbots
Building the fundamentals
Biological inspiration
ANNs
Activation functions
Linear activation
Sigmoid activation
Tanh activation
ReLU activation
Softmax activation
TensorFlow and Keras
Setting up the environment
Introduction to TensorFlow
Installing TensorFlow CPU
Installing TensorFlow GPU
Testing your installation
Getting to know TensorFlow
Building a graph
Creating a Session
Introduction to Keras
Sequential API
Functional API
Summary
Deep Feedforward Networks
Evolutionary path to DFNs
Architecture of DFN
Training
Loss function
Regression loss
Mean squared error (MSE)
Mean absolute error
Classification loss
Cross entropy
Gradient descent
Types of gradient descent
Batch gradient descent
Stochastic gradient descent
Mini-batch gradient descent
Backpropagation
Optimizers
Train test and validation
Training set
Validation set
Test set
Overfitting and regularization
L1 and L2 regularization
Dropout
Early stopping
Building our first DFN
MNIST fashion data
Getting the data
Visualizing data
Normalizing and splitting data
Model parameters
One-hot encoding
Building a model graph
Adding placeholders
Adding layers
Adding loss function
Adding an optimizer
Calculating accuracy
Running a session to train
The easy way
Restricted Boltzmann Machines and Autoencoders
