最新章節

• TensorBoard
• Auto differentiation
• Placeholders and feed dictionaries
• Data input
• Scope
• Variables

• TensorBoard 更新時間：2021-07-02 15:17:44
• Auto differentiation
• Placeholders and feed dictionaries
• Data input
• Scope
• Variables
• Session objects
• Graph
• Computational graph
• TensorFlow comparison with Numpy
• Environment setup
• Getting started with TensorFlow
• Summary
• BRNN on TIMIT dataset
• Bidirectional RNNs
• Results
• Defining Twinet
• Example of a named entity recognition
• Named entity recognition using a twisted neural network
• Quantitative results
• Feature histogram baselines
• Resolution improvements
• Large-scale video processing with neural networks
• Results
• Solution
• Problem statement
• Avoiding overfitting in neural networks
• Research in Neural Networks
• Summary
• Comparing the Sparse encoder with the Additive Gaussian Noise encoder
• Sparse autoencoder on MNIST data
• Complete code listing of the sparse autoencoder
• Cost of a sparse autoencoder based on KL Divergence
• KL divergence in TensorFlow
• KL divergence
• Sparse autoencoder
• Additive Gaussian Noise autoencoder summary
• Comparing basic encoder costs with the Additive Gaussian Noise autoencoder
• Additive Gaussian autoencoder full code listing
• Plotting the reconstructed images
• Plotting the weights
• Training the model
• Additive Gaussian Autoencoder with the MNIST dataset
• Autoencoder class
• Additive Gaussian Noise autoencoder
• Basic autoencoder summary
• Basic autoencoder full code listing
• Basic autoencoder recreated images plot
• Basic autoencoder plot of weights
• Basic autoencoders with MNIST data
• AutoEncoder class
• Autoencoder initialization
• Basic autoencoders
• Dataset
• Under-complete autoencoders
• Autoencoder algorithms
• Autoencoders
• Summary
• Classifying the NotMNIST dataset with a DBN
• DBNs with two RBM layers
• Comparing the accuracy metrics
• An RBM layer with 128 neurons
• An RBM layer with 512 neurons
• Effect of the number of neurons in an RBM layer in a DBN
• Output for a DBN with 256-neuron RBN layers
• Input parameters for a DBN with 256-Neuron RBM layers
• Loading the dataset
• DBN implementation for the MNIST dataset
• Finding the accuracy of the model
• Predicting the label
• Model training
• Pretraining the DBN
• RBM class
• Class initialization
• DBN implementation
• Understanding deep belief networks
• Deep Belief Networking
• Summary
• Coupled GAN
• Wasserstein GAN
• Info GAN
• Conditional GAN
• Vanilla GAN
• Types of GANs
• GAN with an example
• GANs
• Sequence models
• GAN
• Autoencoders
• Types of generative models
• Discriminative versus generative models
• Generative models
• Generative Models
• Summary
• Computational graph
• Sentiment analysis with an RNN
• Word embeddings
• Sentiment analysis
• Computational graph
• LSTM implementation
• Life cycle of LSTM
• Introduction to long short term memory networks
• Computational graph
• RNN implementation with TensorFlow
• Computational graph
• RNN implementation
• Introduction to RNNs
• Recurrent Neural Networks
• Summary
• Plotting filters and their effects on an image
• First epoch
• Optimizer
• Defining cost and optimizer
• Fully connected layers
• Flatten the layer
• Third convolution layer
• Second convolution layer
• First convolution layer
• Input tensor
• Defining a tensor for input images and the first convolution layer
• Image classification with convolutional networks
• Average pool
• Example code
• Max pool
• Pooling
• Strides
• Convolution on an image
• Convolution operations
• Image loading in TensorFlow
• CNN in TensorFlow
• Single Conv Layer Computation
• An overview and the intuition of CNN
• Convolutional Neural Networks
• Summary
• Optimization with an example
• Which optimizer to choose
• Algorithms to optimize gradient descent
• Different variants of gradient descent
• Gradient descent
• Types of optimizers
• What is optimization?
• Optimization for Neural Networks
• Summary
• Analyzing the effect of activation functions on the feedforward networks accuracy
• Implementing feedforward networks with images
• Code execution
• Analyzing the Iris dataset
• Implementing feedforward networks with TensorFlow
• Understanding backpropagation
• Defining feedforward networks
• Deep Feedforward Networks
• Summary
• Optimizers
• Optimization
• Determinant
• Hessian
• Gradient
• Calculus
• Principal Component Analysis
• Eigenvalue decomposition
• Singular value decomposition
• Solving linear equations
• Inverse matrix
• Identity matrix
• Matrix diagonals
• Matrix transpose
• Trace operator
• Matrix multiplication
• Matrices
• Vectors
• Operations
• Tensors
• Scalars vectors and matrices
• Linear algebra structures
• Setting up the Python environment in Pycharm
• Environment setup
• Understanding linear algebra
• Maths for Neural Networks
• Questions
• Piracy
• Errata
• Downloading the color images of this book
• Downloading the example code
• Customer support
• Reader feedback
• Conventions
• Who this book is for
• What you need for this book
• What this book covers
• Preface
• Customer Feedback
• Why subscribe?
• www.PacktPub.com
• About the Reviewer
• About the Authors
• Credits
• Neural Network Programming with TensorFlow
• Copyright
• Title Page
• coverpage

• 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
• TensorBoard 更新時間：2021-07-02 15:17:44