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أخواني في الله أحضرت لكم كتاب Practical MATLAB Deep Learning - A Project-Based Approach Michael Paluszek, Stephanie Thomas
![كتاب Practical MATLAB Deep Learning - A Project-Based Approach P_m_d_11](https://i.servimg.com/u/f61/15/36/74/14/p_m_d_11.jpg) و المحتوى كما يلي :
Contents About the Authors XI About the Technical Reviewer XIII Acknowledgements XV 1 What Is Deep Learning? 1 1.1 Deep Learning . 1 1.2 History of Deep Learning 2 1.3 Neural Nets . 4 1.3.1 Daylight Detector . 8 1.3.2 XOR Neural Net . 9 1.4 Deep Learning and Data 16 1.5 Types of Deep Learning 18 1.5.1 Multilayer Neural Network 18 1.5.2 Convolutional Neural Networks (CNN) . 18 1.5.3 Recurrent Neural Network (RNN) 18 1.5.4 Long Short-Term Memory Networks (LSTMs) . 19 1.5.5 Recursive Neural Network 19 1.5.6 Temporal Convolutional Machines (TCMs) . 19 1.5.7 Stacked Autoencoders 19 1.5.8 Extreme Learning Machine (ELM) 19 1.5.9 Recursive Deep Learning . 19 1.5.10 Generative Deep Learning 20 1.6 Applications of Deep Learning . 20 1.7 Organization of the Book 22 2 MATLAB Machine Learning Toolboxes 25 2.1 Commercial MATLAB Software . 25 2.1.1 MathWorks Products . 25 2.2 MATLAB Open Source 27 2.2.1 Deep Learn Toolbox . 28 2.2.2 Deep Neural Network . 28 IIICONTENTS 2.2.3 MatConvNet . 28 2.2.4 Pattern Recognition and Machine Learning Toolbox (PRMLT) . 28 2.3 XOR Example . 28 2.4 Training . 37 2.5 Zermelo’s Problem . 38 3 Finding Circles with Deep Learning 43 3.1 Introduction . 43 3.2 Structure 43 3.2.1 imageInputLayer . 44 3.2.2 convolution2dLayer . 44 3.2.3 batchNormalizationLayer . 46 3.2.4 reluLayer . 46 3.2.5 maxPooling2dLayer . 47 3.2.6 fullyConnectedLayer . 48 3.2.7 softmaxLayer . 49 3.2.8 classificationLayer 49 3.2.9 Structuring the Layers 50 3.3 Generating Data: Ellipses and Circles . 51 3.3.1 Problem 51 3.3.2 Solution 51 3.3.3 How It Works . 51 3.4 Training and Testing 55 3.4.1 Problem 55 3.4.2 Solution 56 3.4.3 How It Works . 56 4 Classifying Movies 65 4.1 Introduction . 65 4.2 Generating a Movie Database . 65 4.2.1 Problem 65 4.2.2 Solution 65 4.2.3 How It Works . 65 4.3 Generating a Movie Watcher Database . 68 4.3.1 Problem 68 4.3.2 Solution 68 4.3.3 How It Works . 68 4.4 Training and Testing 70 4.4.1 Problem 70 4.4.2 Solution 70 4.4.3 How It Works . 71 IVCONTENTS 5 Algorithmic Deep Learning 77 5.1 Building a Detection Filter . 81 5.1.1 Problem 81 5.1.2 Solution 81 5.1.3 How It Works . 82 5.2 Simulating Fault Detection . 84 5.2.1 Problem 84 5.2.2 Solution 84 5.2.3 How It Works . 84 5.3 Testing and Training 87 5.3.1 Problem 87 5.3.2 Solution 87 5.3.3 How It Works . 88 6 Tokamak Disruption Detection 91 6.1 Introduction . 91 6.2 Numerical Model 93 6.2.1 Dynamics . 93 6.2.2 Sensors 96 6.2.3 Disturbances . 96 6.2.4 Controller . 98 6.3 Dynamical Model 100 6.3.1 Problem 100 6.3.2 Solution 100 6.3.3 How It Works . 100 6.4 Simulate the Plasma 102 6.4.1 Problem 102 6.4.2 Solution 102 6.4.3 How It Works . 103 6.5 Control the Plasma . 104 6.5.1 Problem 104 6.5.2 Solution 106 6.5.3 How It Works . 106 6.6 Training and Testing 107 6.6.1 Problem 107 6.6.2 Solution 107 6.6.3 How It Works . 108 7 Classifying a Pirouette 115 7.1 Introduction . 115 7.1.1 Inertial Measurement Unit 117 7.1.2 Physics 118 VCONTENTS 7.2 Data Acquisition 120 7.2.1 Problem 120 7.2.2 Solution 120 7.2.3 How It Works . 121 7.3 Orientation . 126 7.3.1 Problem 126 7.3.2 Solution 126 7.3.3 How It Works . 126 7.4 Dancer Simulation . 128 7.4.1 Problem 128 7.4.2 Solution 128 7.4.3 How It Works . 128 7.5 Real-Time Plotting . 132 7.5.1 Problem 132 7.5.2 Solution 132 7.5.3 How It Works . 132 7.6 Quaternion Display . 134 7.6.1 Problem 134 7.6.2 Solution 135 7.6.3 How It Works . 135 7.7 Data Acquisition GUI 138 7.7.1 Problem 138 7.7.2 Solution 138 7.7.3 How It Works . 138 7.8 Making the IMU Belt 146 7.8.1 Problem 146 7.8.2 Solution 146 7.8.3 How It Works . 146 7.9 Testing the System . 147 7.9.1 Problem 147 7.9.2 Solution 147 7.9.3 How It Works . 147 7.10 Classifying the Pirouette 149 7.10.1 Problem 149 7.10.2 Solution 149 7.10.3 How It Works . 150 7.11 Hardware Sources 154 8 Completing Sentences 155 8.1 Introduction . 155 8.1.1 Sentence Completion . 155 8.1.2 Grammar . 156 VICONTENTS 8.1.3 Sentence Completion by Pattern Recognition 157 8.1.4 Sentence Generation . 157 8.2 Generating a Database of Sentences 157 8.2.1 Problem 157 8.2.2 Solution 157 8.2.3 How It Works . 157 8.3 Creating a Numeric Dictionary . 159 8.3.1 Problem 159 8.3.2 Solution 159 8.3.3 How It Works . 159 8.4 Map Sentences to Numbers . 160 8.4.1 Problem 160 8.4.2 Solution 160 8.4.3 How It Works . 160 8.5 Converting the Sentences . 161 8.5.1 Problem 161 8.5.2 Solution 161 8.5.3 How It Works . 162 8.6 Training and Testing 163 8.6.1 Problem 163 8.6.2 Solution 164 8.6.3 How It Works . 164 9 Terrain-Based Navigation 169 9.1 Introduction . 169 9.2 Modeling Our Aircraft . 169 9.2.1 Problem 169 9.2.2 Solution 169 9.2.3 How It Works . 169 9.3 Generating a Terrain Model 177 9.3.1 Problem 177 9.3.2 Solution 177 9.3.3 How It Works . 177 9.4 Close Up Terrain 182 9.4.1 Problem 182 9.4.2 Solution 182 9.4.3 How It Works . 182 9.5 Building the Camera Model 183 9.5.1 Problem 183 9.5.2 Solution 183 9.5.3 How It Works . 184 9.6 Plot Trajectory over an Image . 187 VIICONTENTS 9.6.1 Problem 187 9.6.2 Solution 187 9.6.3 How It Works . 187 9.7 Creating the Test Images 190 9.7.1 Problem 190 9.7.2 Solution 190 9.7.3 How It Works . 190 9.8 Training and Testing 193 9.8.1 Problem 193 9.8.2 Solution 193 9.8.3 How It Works . 193 9.9 Simulation 197 9.9.1 Problem 197 9.9.2 Solution 197 9.9.3 How It Works . 197 10 Stock Prediction 203 10.1 Introduction . 203 10.2 Generating a Stock Market . 203 10.2.1 Problem 203 10.2.2 Solution 203 10.2.3 How It Works . 203 10.3 Create a Stock Market . 207 10.3.1 Problem 207 10.3.2 Solution 208 10.3.3 How It Works . 208 10.4 Training and Testing 210 10.4.1 Problem 210 10.4.2 Solution 210 10.4.3 How It Works . 210 11 Image Classification 219 11.1 Introduction . 219 11.2 Using a Pretrained Network 219 11.2.1 Problem 219 11.2.2 Solution 219 11.2.3 How It Works . 219 12 Orbit Determination 227 12.1 Introduction . 227 12.2 Generating the Orbits 227 12.2.1 Problem 227 VIIICONTENTS 12.2.2 Solution 227 12.2.3 How It Works . 227 12.3 Training and Testing 234 12.3.1 Problem 234 12.3.2 Solution 234 12.3.3 How It Works . 235 12.4 Implementing an LSTM 239 12.4.1 Problem 239 12.4.2 Solution 239 12.4.3 How It Works . 239 12.5 Conic Sections . 243 Bibliography 247 Index 24 Index A Aircraft model, 169, 170 Algorithmic Deep Learning Neural Network (ADLNN), 77, 78 air turbine, 77, 79 AirTurbineSim.m, 79, 80 algorithmic filter/estimator, 80 pressure regulator input, 81 B Bidirectional long short-term memory (biLSTM), 107, 152, 213, 241 C Camera model, 183 Classify function, 57 Commercial software, 25–27 Convolutional network layer types batchNormalizationLayer, 46 classificationLayer, 49 convolution2dLayer, 44–46 fullyConnectedLayer, 48 imageInputLayer, 44 maxPooling2dLayer, 47 reluLayer, 46–48 softmaxLayer, 49 one-set, window, 63 structuring, 50 Convolutional neural networks (CNN), 18, 28, 193 Convolution process, 45 Cross-entropy loss, 49 D Dancer simulation RHSDancer.m, 128 data structure, 128 double pirouette, simulation of, 131 linear acceleration, 130 parameters, 129 Data acquisition CUI, 138–146 Data acquisition system, 147–149 Daylight detector, 8–9 Deep learning system applications, 20–21 camera model, building, 183 complete sentences, 163 data, 16–18 defined, 1 detection filter, 22 history, 2–3 network, 24 orientation, 126–127 types, 18–20 Deep Learn Toolbox, 28 Deep Neural Network, 28 Detection filter air turbine, failures, 81 DetectionFilter.m, 82, 83 reset action, 84 specific gain matrix, 82 time constant, 82 Diamagnetic energy, 92 E Edge localized mode (ELM), 93, 96, 97 Ellipses and circles generate images, Ellipses and circles (cont.) train and test, 55–62 ELM, see Extreme learning machine (ELM) Euler’s equation, 118 Exclusive-or (XOR), 2, 9 activation function, 11 DLXOR.m script, 28–29 feedforwardnet, 37 Gaussian noise, 37 GUI, 29–30 hidden layers, 35, 36 mean output error, 15, 16 network training histogram, 33 performance, 31 state, 32 neural net, 35 regression, 34 tansig, 35 truth table and solution networks, 10 weights, expand, 12 XORDemo, 11, 14 XOR.m, 10–11 XORTraining.m, 12–13 Extreme learning machine (ELM), 19 F Fault detection simulation detection filter, 86 DetectionFilterSim, 84, 85 failed tachometer, 87 regulator, fail, 85, 86 fminsearch, 173 fullyConnectedLayer, 48, 213 G Generative Deep Learning, 20, 157 H Handwriting analysis, 20 Hessian matrix, 37, 38 I Image classification, 217 Image recognition, 20 IMU belt, 146–147 Inertial Measurement Unit (IMU), 117–118 International Tokamak Experimental Reactor (ITER), 91 Joint European Torus (JET), 95 L Levenberg Marquardt training algorithm, 37 Long short-term memory (LSTM) network, 19, 210, 239 lstmLayer (numHiddenUnits), 213 Lumped parameter model, 94 M Machine learning, types, 2 Machine translation, 3, 20 Magnetohydrodynamic (MHD), 92 MatConvNet, 28 MathWorks products Computer Vision System Toolbox, 27 Deep Learning toolbox, 26 Image Acquisition Toolbox, 27 Instrument Control Toolbox, 26 Parallel Computing Toolbox, 27 Statistics and Machine Learning Toolbox, 26 Text Analytics Toolbox, 27 visualization tools, 25 Movie database characteristics, 66 function demo, 67 generate, 65–68 viewer database, 71 Movie watchers generate, 68–70 training window, 74, 76 Multilayer network, 1–3 250 J, KINDEX N Neural nets neuron, 4 activation functions, 5, 6 LinearNeuron.m., 6, 7 threshold function, 7 two input, 4 Neural network research, 1 O Open source tools, 27–28 Orbit determination conic sections, 243–245 generation Elliptical orbit, 229 Keplerian elements, 230–233 orbital motion, 229 test orbit, 234 theta, 228 two conics, a circle and ellipse, 227 LSTM, implementation, 239–242 test results, 242 training window, 242 validation data, 240 xTrain, 239–240 training and testing, 235–238 P Patternnet network, 73 input/output, 75 training window, 76 Pattern Recognition and Machine Learning Toolbox (PRMLT), 28 Pirouette, 115 baseball pitcher’s pitch, 116 center of mass, dancer, 119 classification, 149–150 bilstmLayer, 152 DancerNN.m, 150–151 neural net training, 154 testing neural network, 153 data acquisition BluetoothTest.m, 124–125 communication state status, 122 instrumental control toolbox, 121 Mac dongle, 121 MATLAB Bluetooth function, 120 replying data, 122 IMU, 117–118 instrument control toolbox, 115 physics, 118–119 sources of hardware, 154 Q Quadratic error, 11 Quaternion display Ballerina.obj file, 135 dancer orientation, 138 QuaternionVisualization.m, 136 real time plots, 135 Quaternion operations, 126–127 R rand, 16 randi, 57, 88 Real-time plotting, 131–134 Recurrent Neural Network (RNN), 18–19 Recursive Deep Learning, 19 regressionLayer, 213 Replaced recursive neural nets (RNNs), 19, 210 reshape, 17 Root-mean-square error (RMSE), 214, 241 S sequenceInputLayer (inputSize), 213 Single-layer networks, 1, 2 Speech recognition, 20 Stacked autoencoders, 19 Stock prediction algorithm generation function PlotStock.m plots, 205 Geometric Brownian Motion, 203 high volatility, 207 multiple stocks, creation, 204 US stocks, 204 251INDEX Stock prediction algorithm (cont.) zero volatility, 206 stock market, creation, 208, 209 training and testing bilstm layer, 216 LSTM layer, 210, 216, 217 neural net, layers, 213 predictAndUpdateState, 214, 215 RMSE, 214 RNNs, 210 stock price, 211, 212 training window, 214 Support Vector Machines (SVM), 3 Targeting, 20 Temporal convolutional machines (TCMs), 19 TensorFlow, 3 Terrain-Based navigation aircraft model dynamical model, 172 fminsearch, 173 Gulfstream, 174 lift, drag, and gravity, 171, 172 North-East-Up coordinates, velocity, 169, 170 numerical integration, 175 output, 176 trajectory, 177 camera model, building Pinhole camera, 184, 185 source image and view, 186, 187 close up terrain, 182–183 generating terrain model, 177–181 Plot Trajectory, over image, 187–189 simulation camera view and trajectory, 199 subplot, 197–198 terrain segments and aircraft path, 200, 201 test image, creation, 190–192 training and testing, 193–196 Testing and training DetectionFilterNN.m, 88–89 faults, characterize, 87 feedforwardnet, 88 GUI, 89, 90 XOR problem, 87 Tokamaks disruptions dynamical model, 99–102 factors, 91–93 numerical model controller, 98–99 disturbances, 96–97 dynamics, 93–95 sensors, 96 plasma control, 104, 106–107 simulation, 102–105, 108 train and test, 107–113 trainNetwork function, 56 Z Zermelo’s problem control angle, 40 cost, 41 costate equations, 40 defined, 38 Hamiltonian, 39 local and global minimums, 39 solutions, 41 #ماتلاب,#متلاب,#Matlab,
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