更新时间：2021-06-24 15:46:36
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Hands-On Neural Networks with Keras
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Preface
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What this book covers
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Section 1: Fundamentals of Neural Networks
Overview of Neural Networks
Defining our goal
Knowing our tools
Keras
TensorFlow
The fundamentals of neural learning
What is a neural network?
Observing the brain
Building a biological brain
The physiology of a neuron
Representing information
The mysteries of neural encoding
Distributed representation and learning
The fundamentals of data science
Information theory
Entropy
Cross entropy
The nature of data processing
From data science to ML
Modeling data in high-dimensional spaces
The curse of dimensionality
Algorithmic computation and predictive models
Matching a model to use cases
Functional representations
The pitfalls of ML
Unbalanced class priors
Underfitting
Overfitting
Bad data
Irrelevant features and labels
Summary
Further reading
A Deeper Dive into Neural Networks
From the biological to the artificial neuron – the perceptron
Building a perceptron
Input
Weights
Summation
Introducing non-linearity
Activation functions
Understanding the role of the bias term
Output
Learning through errors
The mean squared error loss function
Training a perceptron
Quantifying loss
Loss as a function of model weights
Backpropagation
Computing the gradient
The learning rate
Scaling the perceptron
A single layered network
Experimenting with TensorFlow playground
Capturing patterns heirarchically
Steps ahead
Signal Processing - Data Analysis with Neural Networks
Processing signals
Representational learning
Avoiding random memorization
Representing signals with numbers
Images as numbers
Feeding a neural network
Examples of tensors
Dimensionality of data
Making some imports
Keras's sequential API
Loading the data
Checking the dimensions
Building a model
Introducting Keras layers
Initializing weights
Keras activations
Summarizing your model visually
Compiling the model
Fitting the model
Evaluating model performance
Regularization
Adjusting network size
