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Neural Network Programming with TensorFlow

更新時間：2021-07-02 15:17:44

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Thisbookismeantfordeveloperswithastatisticalbackgroundwhowanttoworkwithneuralnetworks.ThoughwewillbeusingTensorFlowastheunderlyinglibraryforneuralnetworks,bookcanbeusedasagenericresourcetobridgethegapbetweenthemathandtheimplementationofdeeplearning.IfyouhavesomeunderstandingofTensorflowandPythonandwanttolearnwhathappensatalevellowerthantheplainAPIsyntax,thisbookisforyou.

目錄(200章)

倒序

coverpage
Title Page
Copyright
Neural Network Programming with TensorFlow
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
Maths for Neural Networks
Understanding linear algebra
Environment setup
Setting up the Python environment in Pycharm
Linear algebra structures
Scalars vectors and matrices
Tensors
Operations
Vectors
Matrices
Matrix multiplication
Trace operator
Matrix transpose
Matrix diagonals
Identity matrix
Inverse matrix
Solving linear equations
Singular value decomposition
Eigenvalue decomposition
Principal Component Analysis
Calculus
Gradient
Hessian
Determinant
Optimization
Optimizers
Summary
Deep Feedforward Networks
Defining feedforward networks
Understanding backpropagation
Implementing feedforward networks with TensorFlow
Analyzing the Iris dataset
Code execution
Implementing feedforward networks with images
Analyzing the effect of activation functions on the feedforward networks accuracy
Summary
Optimization for Neural Networks
What is optimization?
Types of optimizers
Gradient descent
Different variants of gradient descent
Algorithms to optimize gradient descent
Which optimizer to choose
Optimization with an example
Summary
Convolutional Neural Networks
An overview and the intuition of CNN
Single Conv Layer Computation
CNN in TensorFlow
Image loading in TensorFlow
Convolution operations
Convolution on an image
Strides
Pooling
Max pool
Example code
Average pool
Image classification with convolutional networks
Defining a tensor for input images and the first convolution layer
Input tensor
First convolution layer
Second convolution layer
Third convolution layer
Flatten the layer
Fully connected layers
Defining cost and optimizer
Optimizer
First epoch
Plotting filters and their effects on an image
Summary
Recurrent Neural Networks
Introduction to RNNs
RNN implementation
Computational graph
RNN implementation with TensorFlow
Computational graph
Introduction to long short term memory networks
Life cycle of LSTM
LSTM implementation
Computational graph
Sentiment analysis
Word embeddings
Sentiment analysis with an RNN
Computational graph
Summary
Generative Models
Generative models
Discriminative versus generative models
Types of generative models
Autoencoders
GAN
Sequence models
GANs
GAN with an example
Types of GANs
Vanilla GAN
Conditional GAN
Info GAN
Wasserstein GAN
Coupled GAN
Summary
Deep Belief Networking
Understanding deep belief networks
DBN implementation
Class initialization
RBM class
Pretraining the DBN
Model training
Predicting the label
Finding the accuracy of the model
DBN implementation for the MNIST dataset
Loading the dataset
Input parameters for a DBN with 256-Neuron RBM layers
Output for a DBN with 256-neuron RBN layers
Effect of the number of neurons in an RBM layer in a DBN
An RBM layer with 512 neurons
An RBM layer with 128 neurons
Comparing the accuracy metrics
DBNs with two RBM layers
Classifying the NotMNIST dataset with a DBN
Summary
Autoencoders
Autoencoder algorithms
Under-complete autoencoders
Dataset
Basic autoencoders
Autoencoder initialization
AutoEncoder class
Basic autoencoders with MNIST data
Basic autoencoder plot of weights
Basic autoencoder recreated images plot
Basic autoencoder full code listing
Basic autoencoder summary
Additive Gaussian Noise autoencoder
Autoencoder class
Additive Gaussian Autoencoder with the MNIST dataset
Training the model
Plotting the weights
Plotting the reconstructed images
Additive Gaussian autoencoder full code listing
Comparing basic encoder costs with the Additive Gaussian Noise autoencoder
Additive Gaussian Noise autoencoder summary
Sparse autoencoder
KL divergence
KL divergence in TensorFlow
Cost of a sparse autoencoder based on KL Divergence
Complete code listing of the sparse autoencoder
Sparse autoencoder on MNIST data
Comparing the Sparse encoder with the Additive Gaussian Noise encoder
Summary
Research in Neural Networks
Avoiding overfitting in neural networks
Problem statement
Solution
Results
Large-scale video processing with neural networks
Resolution improvements
Feature histogram baselines
Quantitative results
Named entity recognition using a twisted neural network
Example of a named entity recognition
Defining Twinet
Results
Bidirectional RNNs
BRNN on TIMIT dataset
Summary
Getting started with TensorFlow
Environment setup
TensorFlow comparison with Numpy
Computational graph
Graph
Session objects
Variables
Scope
Data input
Placeholders and feed dictionaries
Auto differentiation
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