【新书推荐】【2020】MATLAB深度学习工具箱用户指南

Table of contents :
Deep Networks
Deep Learning in MATLAB
What Is Deep Learning?
Try Deep Learning in 10 Lines of MATLAB Code
Start Deep Learning Faster Using Transfer Learning
Train Classifiers Using Features Extracted from Pretrained Networks
Deep Learning with Big Data on CPUs, GPUs, in Parallel, and on the Cloud
Deep Learning with Big Data on GPUs and in Parallel
Training with Multiple GPUs
Deep Learning in the Cloud
Fetch and Preprocess Data in Background
Pretrained Deep Neural Networks
Compare Pretrained Networks
Load Pretrained Networks
Feature Extraction
Transfer Learning
Import and Export Networks
Learn About Convolutional Neural Networks
Multiple-Input and Multiple-Output Networks
Multiple-Input Networks
Multiple-Output Networks
List of Deep Learning Layers
Deep Learning Layers
Specify Layers of Convolutional Neural Network
Image Input Layer
Convolutional Layer
Batch Normalization Layer
ReLU Layer
Cross Channel Normalization (Local Response Normalization) Layer
Max and Average Pooling Layers
Dropout Layer
Fully Connected Layer
Output Layers
Set Up Parameters and Train Convolutional Neural Network
Specify Solver and Maximum Number of Epochs
Specify and Modify Learning Rate
Specify Validation Data
Select Hardware Resource
Save Checkpoint Networks and Resume Training
Set Up Parameters in Convolutional and Fully Connected Layers
Train Your Network
Deep Learning Tips and Tricks
Choose Network Architecture
Choose Training Options
Improve Training Accuracy
Fix Errors in Training
Prepare and Preprocess Data
Use Available Hardware
Fix Errors With Loading from MAT-Files
Long Short-Term Memory Networks
LSTM Network Architecture
Layers
Classification, Prediction, and Forecasting
Sequence Padding, Truncation, and Splitting
Normalize Sequence Data
Out-of-Memory Data
Visualization
LSTM Layer Architecture
Deep Network Designer
Transfer Learning with Deep Network Designer
Build Networks with Deep Network Designer
Open App and Import Networks
Create and Edit a Network
Check Network
Train Network Using Deep Network Designer
Export Network
Create Simple Sequence Classification Network Using Deep Network Designer
Generate MATLAB Code from Deep Network Designer
Generate MATLAB Code to Recreate Network Layers
Generate MATLAB Code to Train Network
Deep Learning with Images
Classify Webcam Images Using Deep Learning
Train Deep Learning Network to Classify New Images
Train Residual Network for Image Classification
Classify Image Using GoogLeNet
Extract Image Features Using Pretrained Network
Transfer Learning Using AlexNet
Create Simple Deep Learning Network for Classification
Train Convolutional Neural Network for Regression
Train Network with Multiple Outputs
Convert Classification Network into Regression Network
Train Generative Adversarial Network (GAN)
Train Conditional Generative Adversarial Network (CGAN)
Train a Siamese Network to Compare Images
Train a Siamese Network for Dimensionality Reduction
Train Variational Autoencoder (VAE) to Generate Images
Deep Learning with Time Series, Sequences, and Text
Sequence Classification Using Deep Learning
Time Series Forecasting Using Deep Learning
Speech Command Recognition Using Deep Learning
Sequence-to-Sequence Classification Using Deep Learning
Sequence-to-Sequence Regression Using Deep Learning
Classify Videos Using Deep Learning
Sequence-to-Sequence Classification Using 1-D Convolutions
Classify Text Data Using Deep Learning
Classify Text Data Using Convolutional Neural Network
Multilabel Text Classification Using Deep Learning
Sequence-to-Sequence Translation Using Attention
Generate Text Using Deep Learning
Pride and Prejudice and MATLAB
Word-By-Word Text Generation Using Deep Learning
Image Captioning Using Attention
Deep Learning Tuning and Visualization
Deep Dream Images Using GoogLeNet
Grad-CAM Reveals the Why Behind Deep Learning Decisions
Understand Network Predictions Using Occlusion
Investigate Classification Decisions Using Gradient Attribution Techniques
Resume Training from Checkpoint Network
Deep Learning Using Bayesian Optimization
Run Multiple Deep Learning Experiments in Parallel
Monitor Deep Learning Training Progress
Customize Output During Deep Learning Network Training
Investigate Network Predictions Using Class Activation Mapping
View Network Behavior Using tsne
Visualize Activations of a Convolutional Neural Network
Visualize Activations of LSTM Network
Visualize Features of a Convolutional Neural Network
Visualize Image Classifications Using Maximal and Minimal Activating Images
Monitor GAN Training Progress and Identify Common Failure Modes
Convergence Failure
Mode Collapse
Manage Deep Learning Experiments
Create a Deep Learning Experiment for Classification
Create a Deep Learning Experiment for Regression
Evaluate Deep Learning Experiments by Using Metric Functions
Try Multiple Pretrained Networks for Transfer Learning
Experiment with Weight Initializers for Transfer Learning
Deep Learning in Parallel and the Cloud
Scale Up Deep Learning in Parallel and in the Cloud
Deep Learning on Multiple GPUs
Deep Learning in the Cloud
Advanced Support for Fast Multi-Node GPU Communication
Deep Learning with MATLAB on Multiple GPUs
Select Particular GPUs to Use for Training
Train Network in the Cloud Using Automatic Parallel Support
Train Network in the Cloud Using Automatic Parallel Support
Use parfeval to Train Multiple Deep Learning Networks
Send Deep Learning Batch Job to Cluster
Train Network Using Automatic Multi-GPU Support
Use parfor to Train Multiple Deep Learning Networks
Upload Deep Learning Data to the Cloud
Train Network in Parallel with Custom Training Loop
Computer Vision Examples
Point Cloud Classification Using PointNet Deep Learning
Import Pretrained ONNX YOLO v2 Object Detector
Export YOLO v2 Object Detector to ONNX
Object Detection Using SSD Deep Learning
Object Detection Using YOLO v3 Deep Learning
Object Detection Using YOLO v2 Deep Learning
Semantic Segmentation Using Deep Learning
Semantic Segmentation Using Dilated Convolutions
Semantic Segmentation of Multispectral Images Using Deep Learning
3-D Brain Tumor Segmentation Using Deep Learning
Define Custom Pixel Classification Layer with Tversky Loss
Train Object Detector Using R-CNN Deep Learning
Object Detection Using Faster R-CNN Deep Learning
Image Processing Examples
Remove Noise from Color Image Using Pretrained Neural Network
Single Image Super-Resolution Using Deep Learning
JPEG Image Deblocking Using Deep Learning
Image Processing Operator Approximation Using Deep Learning
Deep Learning Classification of Large Multiresolution Images
Generate Image from Segmentation Map Using Deep Learning
Neural Style Transfer Using Deep Learning
Automated Driving Examples
Train a Deep Learning Vehicle Detector
Create Occupancy Grid Using Monocular Camera and Semantic Segmentation
Signal Processing Examples
Radar Waveform Classification Using Deep Learning
Pedestrian and Bicyclist Classification Using Deep Learning
Label QRS Complexes and R Peaks of ECG Signals Using Deep Network
Waveform Segmentation Using Deep Learning
Modulation Classification with Deep Learning
Classify ECG Signals Using Long Short-Term Memory Networks
Classify Time Series Using Wavelet Analysis and Deep Learning
Audio Examples
Train Generative Adversarial Network (GAN) for Sound Synthesis
Sequential Feature Selection for Audio Features
Acoustic Scene Recognition Using Late Fusion
Keyword Spotting in Noise Using MFCC and LSTM Networks
Speech Emotion Recognition
Spoken Digit Recognition with Wavelet Scattering and Deep Learning
Cocktail Party Source Separation Using Deep Learning Networks
Voice Activity Detection in Noise Using Deep Learning
Denoise Speech Using Deep Learning Networks
Classify Gender Using LSTM Networks
Reinforcement Learning Examples
Create Simulink Environment and Train Agent
Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation
Create Agent Using Deep Network Designer and Train Using Image Observations
Train DDPG Agent to Control Flying Robot
Train Biped Robot to Walk Using Reinforcement Learning Agents
Train DDPG Agent for Adaptive Cruise Control
Train DQN Agent for Lane Keeping Assist Using Parallel Computing
Train DDPG Agent for Path Following Control
Predictive Maintenance Examples
Chemical Process Fault Detection Using Deep Learning
Automatic Differentiation
Define Custom Deep Learning Layers
Layer Templates
Intermediate Layer Architecture
Check Validity of Layer
Include Layer in Network
Output Layer Architecture
Define Custom Deep Learning Layer with Learnable Parameters
Layer with Learnable Parameters Template
Name the Layer
Declare Properties and Learnable Parameters
Create Constructor Function
Create Forward Functions
Completed Layer
GPU Compatibility
Check Validity of Layer Using checkLayer
Include Custom Layer in Network
Define Custom Deep Learning Layer with Multiple Inputs
Layer with Learnable Parameters Template
Name the Layer
Declare Properties and Learnable Parameters
Create Constructor Function
Create Forward Functions
Completed Layer
GPU Compatibility
Check Validity of Layer with Multiple Inputs
Use Custom Weighted Addition Layer in Network
Define Custom Classification Output Layer
Classification Output Layer Template
Name the Layer
Declare Layer Properties
Create Constructor Function
Create Forward Loss Function
Completed Layer
GPU Compatibility
Check Output Layer Validity
Include Custom Classification Output Layer in Network
Define Custom Weighted Classification Layer
Classification Output Layer Template
Name the Layer
Declare Layer Properties
Create Constructor Function
Create Forward Loss Function
Completed Layer
GPU Compatibility
Check Output Layer Validity
Define Custom Regression Output Layer
Regression Output Layer Template
Name the Layer
Declare Layer Properties
Create Constructor Function
Create Forward Loss Function
Completed Layer
GPU Compatibility
Check Output Layer Validity
Include Custom Regression Output Layer in Network
Specify Custom Layer Backward Function
Create Custom Layer
Create Backward Function
Complete Layer
GPU Compatibility
Specify Custom Output Layer Backward Loss Function
Create Custom Layer
Create Backward Loss Function
Complete Layer
GPU Compatibility
Check Custom Layer Validity
Check Layer Validity
List of Tests
Generated Data
Diagnostics
Specify Custom Weight Initialization Function
Compare Layer Weight Initializers
Assemble Network from Pretrained Keras Layers
Assemble Multiple-Output Network for Prediction
Automatic Differentiation Background
What Is Automatic Differentiation?
Forward Mode
Reverse Mode
Use Automatic Differentiation In Deep Learning Toolbox
Custom Training and Calculations Using Automatic Differentiation
Use dlgradient and dlfeval Together for Automatic Differentiation
Derivative Trace
Characteristics of Automatic Derivatives
Define Custom Training Loops, Loss Functions, and Networks
Define Custom Training Loops
Define Custom Networks
Specify Training Options in Custom Training Loop
Solver Options
Learn Rate
Plots
Verbose Output
Mini-Batch Size
Number of Epochs
Validation
L2 Regularization
Gradient Clipping
Single CPU or GPU Training
Checkpoints
Train Network Using Custom Training Loop
Update Batch Normalization Statistics in Custom Training Loop
Make Predictions Using dlnetwork Object
Train Network Using Model Function
Update Batch Normalization Statistics Using Model Function
Make Predictions Using Model Function
Train Network Using Cyclical Learn Rate for Snapshot Ensembling
List of Functions with dlarray Support
Deep Learning Toolbox Functions with dlarray Support
MATLAB Functions with dlarray Support
Notable dlarray Behaviors
Deep Learning Data Preprocessing
Datastores for Deep Learning
Select Datastore
Input Datastore for Training, Validation, and Inference
Specify Read Size and Mini-Batch Size
Transform and Combine Datastores
Use Datastore for Parallel Training and Background Dispatching
Preprocess Images for Deep Learning
Resize Images Using Rescaling and Cropping
Augment Images for Training with Random Geometric Transformations
Perform Additional Image Processing Operations Using Built-In Datastores
Apply Custom Image Processing Pipelines Using Combine and Transform
Preprocess Volumes for Deep Learning
Read Volumetric Data
Associate Image and Label Data
Preprocess Volumetric Data
Preprocess Data for Domain-Specific Deep Learning Applications
Image Processing Applications
Object Detection
Semantic Segmentation
Signal Processing Applications
Audio Processing Applications
Text Analytics
Develop Custom Mini-Batch Datastore
Overview
Implement MiniBatchable Datastore
Add Support for Shuffling
Validate Custom Mini-Batch Datastore
Augment Images for Deep Learning Workflows Using Image Processing Toolbox
Augment Pixel Labels for Semantic Segmentation
Augment Bounding Boxes for Object Detection
Prepare Datastore for Image-to-Image Regression
Train Network Using Out-of-Memory Sequence Data
Train Network Using Custom Mini-Batch Datastore for Sequence Data
Classify Out-of-Memory Text Data Using Deep Learning
Classify Out-of-Memory Text Data Using Custom Mini-Batch Datastore
Data Sets for Deep Learning
Image Data Sets
Time Series and Signal Data Sets
Video Data Sets
Text Data Sets
Audio Data Sets
Deep Learning Code Generation
Code Generation for Deep Learning Networks
Code Generation for Semantic Segmentation Network
Lane Detection Optimized with GPU Coder
Code Generation for a Sequence-to-Sequence LSTM Network
Deep Learning Prediction on ARM Mali GPU
Code Generation for Object Detection by Using YOLO v2
Integrating Deep Learning with GPU Coder into Simulink
Deep Learning Prediction by Using NVIDIA TensorRT
Deep Learning Prediction by Using Different Batch Sizes
Traffic Sign Detection and Recognition
Logo Recognition Network
Pedestrian Detection
Code Generation for Denoising Deep Neural Network
Train and Deploy Fully Convolutional Networks for Semantic Segmentation
Code Generation for Semantic Segmentation Network by Using U-net
Code Generation for Deep Learning on ARM Targets
Code Generation for Deep Learning on Raspberry Pi
Deep Learning Prediction with ARM Compute Using cnncodegen
Deep Learning Prediction with Intel MKL-DNN
Generate C++ Code for Object Detection Using YOLO v2 and Intel MKL-DNN
Code Generation and Deployment of MobileNet-v2 Network to Raspberry Pi
Neural Network Design Book
Neural Network Objects, Data, and Training Styles
Workflow for Neural Network Design
Four Levels of Neural Network Design
Neuron Model
Simple Neuron
Transfer Functions
Neuron with Vector Input
Neural Network Architectures
One Layer of Neurons
Multiple Layers of Neurons
Input and Output Processing Functions
Create Neural Network Object
Configure Shallow Neural Network Inputs and Outputs
Understanding Shallow Network Data Structures
Simulation with Concurrent Inputs in a Static Network
Simulation with Sequential Inputs in a Dynamic Network
Simulation with Concurrent Inputs in a Dynamic Network
Neural Network Training Concepts
Incremental Training with adapt
Batch Training
Training Feedback
Multilayer Shallow Neural Networks and Backpropagation Training
Multilayer Shallow Neural Networks and Backpropagation Training
Multilayer Shallow Neural Network Architecture
Neuron Model (logsig, tansig, purelin)
Feedforward Neural Network
Prepare Data for Multilayer Shallow Neural Networks
Choose Neural Network Input-Output Processing Functions
Representing Unknown or Don’t-Care Targets
Divide Data for Optimal Neural Network Training
Create, Configure, and Initialize Multilayer Shallow Neural Networks
Other Related Architectures
Initializing Weights (init)
Train and Apply Multilayer Shallow Neural Networks
Training Algorithms
Training Example
Use the Network
Analyze Shallow Neural Network Performance After Training
Improving Results
Limitations and Cautions
Dynamic Neural Networks
Introduction to Dynamic Neural Networks
How Dynamic Neural Networks Work
Feedforward and Recurrent Neural Networks
Applications of Dynamic Networks
Dynamic Network Structures
Dynamic Network Training
Design Time Series Time-Delay Neural Networks
Prepare Input and Layer Delay States
Design Time Series Distributed Delay Neural Networks
Design Time Series NARX Feedback Neural Networks
Multiple External Variables
Design Layer-Recurrent Neural Networks
Create Reference Model Controller with MATLAB Script
Multiple Sequences with Dynamic Neural Networks
Neural Network Time-Series Utilities
Train Neural Networks with Error Weights
Normalize Errors of Multiple Outputs
Multistep Neural Network Prediction
Set Up in Open-Loop Mode
Multistep Closed-Loop Prediction From Initial Conditions
Multistep Closed-Loop Prediction Following Known Sequence
Following Closed-Loop Simulation with Open-Loop Simulation
Control Systems
Introduction to Neural Network Control Systems
Design Neural Network Predictive Controller in Simulink
System Identification
Predictive Control
Use the Neural Network Predictive Controller Block
Design NARMA-L2 Neural Controller in Simulink
Identification of the NARMA-L2 Model
NARMA-L2 Controller
Use the NARMA-L2 Controller Block
Design Model-Reference Neural Controller in Simulink
Use the Model Reference Controller Block
Import-Export Neural Network Simulink Control Systems
Import and Export Networks
Import and Export Training Data
Radial Basis Neural Networks
Introduction to Radial Basis Neural Networks
Important Radial Basis Functions
Radial Basis Neural Networks
Neuron Model
Network Architecture
Exact Design (newrbe)
More Efficient Design (newrb)
Examples
Probabilistic Neural Networks
Network Architecture
Design (newpnn)
Generalized Regression Neural Networks
Network Architecture
Design (newgrnn)
Self-Organizing and Learning Vector Quantization Networks
Introduction to Self-Organizing and LVQ
Important Self-Organizing and LVQ Functions
Cluster with a Competitive Neural Network
Architecture
Create a Competitive Neural Network
Kohonen Learning Rule (learnk)
Bias Learning Rule (learncon)
Training
Graphical Example
Cluster with Self-Organizing Map Neural Network
Topologies (gridtop, hextop, randtop)
Distance Functions (dist, linkdist, mandist, boxdist)
Architecture
Create a Self-Organizing Map Neural Network (selforgmap)
Training (learnsomb)
Examples
Learning Vector Quantization (LVQ) Neural Networks
Architecture
Creating an LVQ Network
LVQ1 Learning Rule (learnlv1)
Training
Supplemental LVQ2.1 Learning Rule (learnlv2)
Adaptive Filters and Adaptive Training
Adaptive Neural Network Filters
Adaptive Functions
Linear Neuron Model
Adaptive Linear Network Architecture
Least Mean Square Error
LMS Algorithm (learnwh)
Adaptive Filtering (adapt)
Advanced Topics
Neural Networks with Parallel and GPU Computing
Deep Learning
Modes of Parallelism
Distributed Computing
Single GPU Computing
Distributed GPU Computing
Parallel Time Series
Parallel Availability, Fallbacks, and Feedback
Optimize Neural Network Training Speed and Memory
Memory Reduction
Fast Elliot Sigmoid
Choose a Multilayer Neural Network Training Function
SIN Data Set
PARITY Data Set
ENGINE Data Set
CANCER Data Set
CHOLESTEROL Data Set
DIABETES Data Set
Summary
Improve Shallow Neural Network Generalization and Avoid Overfitting
Retraining Neural Networks
Multiple Neural Networks
Early Stopping
Index Data Division (divideind)
Random Data Division (dividerand)
Block Data Division (divideblock)
Interleaved Data Division (divideint)
Regularization
Summary and Discussion of Early Stopping and Regularization
Posttraining Analysis (regression)
Edit Shallow Neural Network Properties
Custom Network
Network Definition
Network Behavior
Custom Neural Network Helper Functions
Automatically Save Checkpoints During Neural Network Training
Deploy Shallow Neural Network Functions
Deployment Functions and Tools for Trained Networks
Generate Neural Network Functions for Application Deployment
Generate Simulink Diagrams
Deploy Training of Shallow Neural Networks
Historical Neural Networks
Historical Neural Networks Overview
Perceptron Neural Networks
Neuron Model
Perceptron Architecture
Create a Perceptron
Perceptron Learning Rule (learnp)
Training (train)
Limitations and Cautions
Linear Neural Networks
Neuron Model
Network Architecture
Least Mean Square Error
Linear System Design (newlind)
Linear Networks with Delays
LMS Algorithm (learnwh)
Linear Classification (train)
Limitations and Cautions
Neural Network Object Reference
Neural Network Object Properties
General
Architecture
Subobject Structures
Functions
Weight and Bias Values
Neural Network Subobject Properties
Inputs
Layers
Outputs
Biases
Input Weights
Layer Weights
Function Approximation, Clustering, and Control Examples
Body Fat Estimation
Crab Classification
Wine Classification
Cancer Detection
Character Recognition
Train Stacked Autoencoders for Image Classification
Iris Clustering
Gene Expression Analysis
Maglev Modeling
Competitive Learning
One-Dimensional Self-organizing Map
Two-Dimensional Self-organizing Map
Radial Basis Approximation
Radial Basis Underlapping Neurons
Radial Basis Overlapping Neurons
GRNN Function Approximation
PNN Classification
Learning Vector Quantization
Linear Prediction Design
Adaptive Linear Prediction
Classification with a 2-Input Perceptron
Outlier Input Vectors
Normalized Perceptron Rule
Linearly Non-separable Vectors
Pattern Association Showing Error Surface
Training a Linear Neuron
Linear Fit of Nonlinear Problem
Underdetermined Problem
Linearly Dependent Problem
Too Large a Learning Rate
Adaptive Noise Cancellation
Shallow Neural Networks Bibliography
Shallow Neural Networks Bibliography
Mathematical Notation
Mathematics and Code Equivalents
Mathematics Notation to MATLAB Notation
Figure Notation
Neural Network Blocks for the Simulink Environment
Neural Network Simulink Block Library
Transfer Function Blocks
Net Input Blocks
Weight Blocks
Processing Blocks
Deploy Shallow Neural Network Simulink Diagrams
Example
Suggested Exercises
Generate Functions and Objects
Code Notes
Deep Learning Toolbox Data Conventions
Dimensions
Variables

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