更新时间：2021-06-24 18:40:56
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Foreword
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Preface
Who this book is for
What this book covers
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Download the example code files
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The Building Blocks of Deep Learning
The deep neural network architectures
Neurons
The neuron linear function
Neuron activation functions
The loss and cost functions in deep learning
The forward propagation process
The back propagation function
Stochastic and minibatch gradient descents
Optimization algorithms for deep learning
Using momentum with gradient descent
The RMSProp algorithm
The Adam optimizer
Deep learning frameworks
What is TensorFlow?
What is Keras?
Popular alternatives to TensorFlow
GPU requirements for TensorFlow and Keras
Installing Nvidia CUDA Toolkit and cuDNN
Installing Python
Installing TensorFlow and Keras
Building datasets for deep learning
Bias and variance errors in deep learning
The train val and test datasets
Managing bias and variance in deep neural networks
K-Fold cross-validation
Summary
Using Deep Learning to Solve Regression Problems
Regression analysis and deep neural networks
Benefits of using a neural network for regression
Drawbacks to consider when using a neural network for regression
Using deep neural networks for regression
How to plan a machine learning problem
Defining our example problem
Loading the dataset
Defining our cost function
Building an MLP in Keras
Input layer shape
Hidden layer shape
Output layer shape
Neural network architecture
Training the Keras model
Measuring the performance of our model
Building a deep neural network in Keras
Measuring the deep neural network performance
Tuning the model hyperparameters
Saving and loading a trained Keras model
Monitoring Network Training Using TensorBoard
A brief overview of TensorBoard
Setting up TensorBoard
Installing TensorBoard
How TensorBoard talks to Keras/TensorFlow
Running TensorBoard
Connecting Keras to TensorBoard
Introducing Keras callbacks
Creating a TensorBoard callback
Using TensorBoard
Visualizing training
Visualizing network graphs
Visualizing a broken network
Using Deep Learning to Solve Binary Classification Problems
Binary classification and deep neural networks
Benefits of deep neural networks
Drawbacks of deep neural networks
Case study – epileptic seizure recognition
Defining our dataset
Loading data
Model inputs and outputs
The cost function
Using metrics to assess the performance
Building a binary classifier in Keras
The input layer
The hidden layers
What happens if we use too many neurons?
What happens if we use too few neurons?
Choosing a hidden layer architecture
Coding the hidden layers for our example
The output layer
Putting it all together
