Big Data And Deep Learning. Examples With Matlab
- Length: 482 pages
- Edition: 1
- Language: English
- Publisher: lulu.com
- Publication Date: 2020-05-31
- ISBN-10: B08VNR4SPW
- ISBN-13: 9788472481961
- Sales Rank: #1747474 (See Top 100 Books)
Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions.Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In a simple case, there might be two sets of neurons: ones that receive an input signal and ones that send an output signal. When the input layer receives an input it passes on a modified version of the input to the next layer. In a deep network, there are many layers between the input and output (and the layers are not made of neurons but it can help to think of it that way), allowing the algorithm to use multiple processing layers, composed of multiple linear and non-linear transformations.Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task (e.g., face recognition or facial expression recognition). One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.Deep learning has been characterized as a buzzword, or a rebranding of neural networks
BID DATA AND DEEP LEARNING INTRODUCTION 1.1 MATLAB AND BIG DATA 1.1.1 Access Data 1.1.2 Explore, Process, and Analyze Data 1.1.3 Develop Predictive Models 1.2 DEEP LEARNING 1.2.1 Definitions 1.2.2 Concepts 1.2.3 Deep learning and neural networks 1.2.4 Deep neural networks 1.2.5 Convolutional neural networks 1.2.6 Recursive neural networks 1.2.7 Long short-term memory 1.2.8 Deep belief networks 1.2.9 Convolutional deep belief networks 1.2.10 Large memory storage and retrieval neural networks 1.2.11 Deep Boltzmann machines 1.2.12 Encoder–decoder networks 1.2.13 Deep learning applications 1.3 DEEP LEARNING WITH MATLAB: NEURAL NETWORK TOOLBOX (DEEP LEARNING TOOLBOX) 1.4 Using DEEP LEARNING Toolbox 1.5 Automatic Script Generation 1.6 DEEP LEARNING Toolbox Applications 1.7 Neural Network Design Steps DEEP LEARNING WITH MATLAB: CONVOLUTIONAL Neural NetworkS. FUNCTIONS 2.2.1 Create an image input layer: imageInputLayer 2.2.2 Create 2-D convolutional layer: convolution2dLayer 2.2.3 Create a Rectified Linear Unit (ReLU) layer: reluLayer 2.2.4 Create a local response normalization layer: crossChannelNormalizationLayer 2.2.5 Create an average pooling layer: averagePooling2dLayer 2.2.6 Create max pooling layer: maxPooling2dLayer 2.2.7 Create fully connected layer: fullyConnectedLayer 2.2.8 Create a dropout layer: dropoutLayer 2.2.9 Create a softmax layer softmaxLayer 2.2.10 Create a classification output layer: classificationLayer 2.3.1 Train network: trainNetwork 2.3.2 Options for training neural network. trainingOptions 2.4 Extract Features and Predict Outcomes. FUNCTIONS 2.4.1 Compute network layer activations: activations 2.4.2 Predict responses using a trained network: predict 2.4.3 Classify data using a trained network: classify DEEP LEARNING WITH MATLAB: CONVOLUTIONAL Neural NetworkS. CLASSES 3.2.1 Network layer: layer 3.3.1 Series network class: SeriesNetwork 3.3.2 Training options for stochastic gradient descent with momentum. TrainingOptionsSGDM 3.4.1 ImageInputLayer class 3.4.2 Convolution2DLayer class 3.4.3 ReLULayer class 3.4.4 CrossChannelNormalizationLayer class 3.4.5 AveragePooling2DLayer class 3.4.6 MaxPooling2DLayer class 3.4.7 FullyConnectedLayer class 3.4.8 DropoutLayer class 3.4.9 SoftmaxLayer class 3.4.10 ClassificationOutputLayer class DEEP LEARNING WITH MATLAB: Image Category Classification 4.1 Overview 4.2 Check System Requirements 4.3 Download Image Data 4.4 Load Images 4.5 Download Pre-trained Convolutional Neural Network (CNN) 4.6 Load Pre-trained CNN 4.7 Pre-process Images For CNN 4.8 Prepare Training and Test Image Sets 4.9 Extract Training Features Using CNN 4.10 Train A Multiclass SVM Classifier Using CNN Features 4.11 Evaluate Classifier 4.12 Try the Newly Trained Classifier on Test Images 4.13 References DEEP LEARNING WITH MATLAB: Transfer Learning Using Convolutional Neural Networks AND PRETRAINED Convolutional Neural Networks 5.1 Transfer Learning Using Convolutional Neural Networks 5.2 Pretrained Convolutional Neural Network DEEP LEARNING WITH MATLAB: FunctionS FOR PATTERN RECOGNITION AND CLASSIFICATION. AUTOENCODER 6.1 INTRODUCTION 6.2 view NEURAL NETWORK 6.3 Pattern Recognition and Learning Vector Quantization 6.3.1 Pattern recognition network: patternnet 6.3.2 Learning vector quantization neural network: lvqnet 6.4 Training Options and Network Performance 6.4.1 Receiver operating characteristic: roc 6.4.2 Plot receiver operating characteristic: plotroc 6.4.3 Plot classification confusion matrix: plotconfusion 6.4.4 Neural network performance: crossentropy 6.4.5 Construct and Train a Function Fitting Network 6.4.6 Create and train Feedforward Neural Network 6.4.7 Create and Train a Cascade Network 6.5 Network performance 6.5.1 Description 6.5.2 Examples 6.6 Fit Regression Model and Plot Fitted Values versus Targets 6.6.1 Description 6.6.2 Examples 6.7 Plot Output and Target Values 6.7.1 Description 6.7.2 Examples 6.8 Plot Training State Values 6.9 Plot Performances 6.10 Plot Histogram of Error Values 6.10.1 Syntax 6.10.2 Description 6.10.3 Examples 6.11 Generate MATLAB function for simulating neural network 6.11.1 Create Functions from Static Neural Network 6.11.2 Create Functions from Dynamic Neural Network 6.12 A COMPLETE EXAMPLE: House Price Estimation 6.12.1 The Problem: Estimate House Values 6.12.2 Why Neural Networks? 6.12.3 Preparing the Data 6.12.4 Fitting a Function with a Neural Network 6.12.5 Testing the Neural Network 6.13 Autoencoder class 6.14 Autoencoder FUNCTIONS 6.14.1 Functions 6.14.2 trainAutoencoder 6.14.3 decode 6.14.4 encode 6.14.5 predict 6.14.6 stack 6.14.7 generateFunction 6.14.8 generateSimulink 6.14.9 plotWeights 6.14.10 view 6.15 Construct Deep Network Using Autoencoders DEEP LEARNING WITH MATLAB: MULTILAYER Neural Network 7.1 Create, Configure, and Initialize Multilayer Neural Networks 7.1.1 Other Related Architectures 7.2 FUNCTIONS FOR Create, Configure, and Initialize Multilayer Neural Networks 7.2.1 Initializing Weights (init) 7.2.2 feedforwardnet 7.2.3 configure 7.2.4 init 7.2.5 train 7.2.6 trainlm 7.2.7 tansig 7.2.8 purelin 7.2.9 cascadeforwardnet 7.2.10 patternnet 7.3 Train and Apply Multilayer Neural Networks 7.3.1 Training Algorithms 7.3.2 Training Example 7.3.3 Use the Network 7.4 Train ALGORITMS IN Multilayer Neural Networks 7.4.1 trainbr:Bayesian Regularization 7.4.2 trainscg: Scaled conjugate gradient backpropagation 7.4.3 trainrp: Resilient backpropagation 7.4.4 trainbfg: BFGS quasi-Newton backpropagation 7.4.5 traincgb: Conjugate gradient backpropagation with Powell-Beale restarts 7.4.6 traincgf: Conjugate gradient backpropagation with Fletcher-Reeves updates 7.4.7 traincgp: Conjugate gradient backpropagation with Polak-Ribiére updates 7.4.8 trainoss: One-step secant backpropagation 7.4.9 traingdx: Gradient descent with momentum and adaptive learning rate backpropagation 7.4.10 traingdm: Gradient descent with momentum backpropagation 7.4.11 traingd: Gradient descent backpropagation DEEP LEARNING WITH MATLAB: ANALYZE AND DEPLOY TRAINED NEURAL NETWORK 8.1 ANALYZE NEURAL NETWORK PERFORMANCE 8.2 Improving Results 8.3 Deployment Functions and Tools for Trained Networks 8.4 Generate Neural Network Functions for Application Deployment 8.5 Deploy Neural Network Simulink Diagrams 8.5.1 Example 8.5.2 Suggested Exercises 8.6 Deploy Training of Neural Networks TRAINING SCALABILITY AND EFICIENCE 9.1 Neural Networks with Parallel and GPU Computing 9.1.1 Modes of Parallelism 9.1.2 Distributed Computing 9.1.3 Single GPU Computing 9.1.4 Distributed GPU Computing 9.1.5 Deep Learning 9.1.6 Parallel Time Series 9.1.7 Parallel Availability, Fallbacks, and Feedback 9.2 Automatically Save Checkpoints During Neural Network Training 9.3 Optimize Neural Network Training Speed and Memory 9.3.1 Memory Reduction 9.3.2 Fast Elliot Sigmoid DEEP LEARNING WITH MATLAB: OPTIMAL SOLUTIONS 10.1 Representing Unknown or Don’t-Care Targets 10.1.1 Choose Neural Network Input-Output Processing Functions 10.1.2 Representing Unknown or Don’t-Care Targets 10.2 Configure Neural Network Inputs and Outputs 10.3 Divide Data for Optimal Neural Network Training 10.4 Choose a Multilayer Neural Network Training Function 10.4.1 SIN Data Set 10.4.2 PARITY Data Set 10.4.3 ENGINE Data Set 10.4.4 CANCER Data Set 10.4.5 CHOLESTEROL Data Set 10.4.6 DIABETES Data Set 10.4.7 Summary 10.5 Improve Neural Network Generalization and Avoid Overfitting 10.5.1 Retraining Neural Networks 10.5.2 Multiple Neural Networks 10.5.3 Early Stopping 10.5.4 Index Data Division (divideind) 10.5.5 Random Data Division (dividerand) 10.5.6 Block Data Division (divideblock) 10.5.7 Interleaved Data Division (divideint) 10.5.8 Regularization 10.5.9 Modified Performance Function 10.5.10 Automated Regularization (trainbr) 10.5.11 Summary and Discussion of Early Stopping and Regularization 10.5.12 Posttraining Analysis (regression) 10.6 Train Neural Networks with Error Weights 10.7 Normalize Errors of Multiple Outputs DEEP LEARNING WITH MATLAB: CLASSIFICATION WITH NEURAL NETWORKS. EXAMPLES 11.1 Crab Classification 11.1.1 Why Neural Networks? 11.1.2 Preparing the Data 11.1.3 Building the Neural Network Classifier 11.1.4 Testing the Classifier 11.2 Wine Classification 11.2.1 The Problem: Classify Wines 11.2.2 Why Neural Networks? 11.2.3 Preparing the Data 11.2.4 Pattern Recognition with a Neural Network 11.2.5 Testing the Neural Network 11.3 Cancer Detection 11.3.1 Formatting the Data 11.3.2 Ranking Key Features 11.3.3 Classification Using a Feed Forward Neural Network 11.4 Character Recognition 11.4.1 Creating the First Neural Network 11.4.2 Training the first Neural Network 11.4.3 Training the Second Neural Network 11.4.4 Testing Both Neural Networks DEEP LEARNING WITH MATLAB: AUTOENCODERS AND CLUSTERING WITH NEURAL NETWORKS. EXAMPLES 12.1 Train Stacked Autoencoders for Image Classification 12.1.1 Data set 12.1.2 Training the first autoencoder 12.1.3 Visualizing the weights of the first autoencoder 12.1.4 Training the second autoencoder 12.1.5 Training the final softmax layer 12.1.6 Forming a stacked neural network 12.1.7 Fine tuning the deep neural network 12.1.8 Summary 12.2 Transfer Learning Using Convolutional Neural Networks 12.3 Iris Clustering 12.3.1 Why Self-Organizing Map Neural Networks? 12.3.2 Preparing the Data 12.3.3 Clustering with a Neural Network 12.4 Gene Expression Analysis 12.4.1 The Problem: Analyzing Gene Expressions in Baker’s Yeast (Saccharomyces Cerevisiae) 12.4.2 The Data 12.4.3 Filtering the Genes 12.4.4 Principal Component Analysis 12.4.5 Cluster Analysis: Self-Organizing Maps
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