[1]_Deep_Learning_for_Computer_Vision.pdf 【第1册 - 英

文件名称: [1]_Deep_Learning_for_Computer_Vision.pdf 【第1册 - 英文版】

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详细说明：图像视觉领域的深度学习资料，手把手教你搭建自己的神经网络，让你从实践中深入浅出地学习各种经典神经网络知识。亲试不错，分享之！Deep learning for Computer Vision with Python Starter Bundle Dr. Adrian rosebrock Ist Edition(1. 1.0) Copyright(c2017AdrianRosebrock,PylmageSearch.com PUBLISHED BY PYIMAGESEARCH PYIMAGESEARCH. COM The contents of this book, unless otherwise indicated, are Copyright(C2017 Adrian Rosebrock Pyimage Search com. All rights reserved. Books like this are made possible by the time invested by the authors. If you received this book and did not purchase it, please consider making future books possiblebybuyingacopyathttps://www.pyimagesearch.com/deep-learning-computer-vision- python-book/ today First printing, September 2017 To my father, Joe, my wife, Trish and the family beagles, Josie and Jemma Without their constant love and support this book would not be possible Contents 1 Introduction 15 1.1 I Studied Deep Learning the Wrong Way... This is the Right Way 15 l2 Who this book is for 1. 2. Just Getting Started in Deep Learning 17 1.2.2 Already a Seasoned Deep Learning Practitioner? 1. 3 Book Organization 17 l 3. Volume #l starter bundle 1. 3. 2 Volume #2 Practitioner bundle 111 11 1.3.3 Volume #3: ImageNet Bundle 18 1.3.4 Need to Upgrade Your Bundle? 1.4 Tools of the Trade: Python, Keras, and Mxnet 18 1. 4.1 What about tensorFlow? 18 1.4.2 Do I Need to know OpenCV? 1.5 Developing Our Own Deep Learning Toolset 1.6 Summary 2 What Is Deep Learning? 21 2.1 A Concise History of Neural Networks and Deep Learning 22 2 Hierarchical Feature Learning 24 2.3 How "DeepIs Deep? 27 2.4 Summary 30 3 Image Fundamentals 31 3.1 Pixels: The Building Blocks of Images 31 3.1.1 Forming an Image From Channels 34 3.2 The Image Coordinate System 34 3.2.1 Images as NumPy Arrays 3.2.2 RGB and BGR Ordering 36 3 Scaling and Aspect Ratios 36 3.4 Summary 38 4 Image Classification Basics 39 4.1 What s Image Classification? 4.1.1 A Note on Terminology 40 4.1.2 The Semantic Gap 41 4.1.3 Challenges ,,,,,,42 4.2 Types of Learning 4.2.1 Supervised Learning 45 4.2.2 Unsupervised Learning 4.2.3 Semi-supervised Learning 47 4.3 The Deep Learning Classification Pipeline 4.3.1 A Shift in mindset 48 4.3.2 Step #1: Gather Your dataset 4.3.3 Step #2 Split Your dataset 4.3. 4 Step #3: Train Your Network 5 4.3.5 Step #4: Evaluate 51 4.3.6 Feature-based Learning versus Deep Learning for Image Classification 51 4.3.7 What Happens When my Predictions Are Incorrect? 52 44Summ。ry 52 5 Datasets for Image Classification 哪量量 53 5 MNIST 53 5.2 Animals: Dogs, Cats, and Pandas 54 5.3 CIFAR-10 55 5.4 SMILES 55 5.5 Kaggle: Dogs vs Cats 56 5.6 Flowers-17 56 5.7 CALTECH-101 57 5.8 Tiny ImageNet 200 57 5.9 Adience 58 5.10 ImageNet 58 5.10.1 What Is ImageNet? 58 5.10.2 mageNet Large Scale visual Recognition Challenge (ILSVRC) 58 5.11 Kaggle: Facial Expression Recognition Challenge 59 5.12 Indoor cvPr 60 5.13 stanford cars 50 5.14 Summary 6 Configuring Your Development Environment 63 6.1 Libraries and Packages 63 6.1.2 Keras 64 6.1.3 Manet 64 6. 1. 4 OpenCv, sclkit-image, scikit-learn and more 64 6.2 Configuring your Development Environment? 64 6.3 Preconfigured virtual Machine 65 6.4 Cloud-based instances 65 6.5 How to Structure Your Projects 6 6.6 Summary 66 7 Your First Image Classifier 67 7. 1 Working with Image Datasets 67 7.1.1 Introducing the Animals Dataset 67 7.1.2 The start to Our Deep Learning Toolkit 7.1.3 A Basic Image Preprocessor .69 7.1. 4 Building an Image Loader 70 7.2 k-NN: A simple Classifier 72 7.2.1 A Worked k-NN Example 74 7.2.2 k-NN Hyperparameters 75 7. 2.3 mplementing k-NN ,,,,,,75 7.2. 4 k-NN Results 7. 2.5 Pros and cons of k-nn 7. 3 Summary 80 8 Parameterized Learning 81 8.1 An Introduction to linear classificafion 82 8.1.1 Four Components of Parameterized Learning ,,,82 8.1.2 Linear Classification: From Images to Labels 83 8.1.3 Advantages of Parameterized Learning and Linear classification 84 8. 1. 4 A Simple Linear Classifier With Python 85 8.2 The role of loss functions 88 8. 2.1 What Are Loss functions? ,88 8.2.2 Multi-claSS SVM loss 89 8.2.3 CrOSS-entropy Loss and Softmax Classifiers 91 8.3 Summary 94 Optimization Methods and Regularization 95 9.1 Gradient descent 96 The loss landscape and optimization Surface 96 9.1.2 The Gradient in gradient descent ..97 9.1.3 Treat It Like a Convex Problem (Even if it's not) 9.L4 The bias trick 9.1.5 Pseudocode for gradient descent 9.1. 6 mplementing Basic Gradient Descent in Python 9.1.7 Simple Gradient Descent Results 104 9.2 Stochastic Gradient Descent (SGD) 106 9.2.1 Mini-batch SGD 106 9.2.2 mplementing Mini-batch SGD 107 9,2.3 SGD Results,,,,,,,,,,,, ,110 9.3 Extensions to sGD 111 9. 3. Momentum 9.3.2 Nesteroy's Acceleration .112 9.3.3 Anecdotal recommendations 113 9. 4 Regularization 1l3 9.4.1 What Is Regularization and Why Do We Need It? 113 9.4.2 Updating Our Loss and Weight Update To Include Regularization 115 9.4.3 Types of Regularization Techniques 116 9.4.4 Regularization Applied to Image Classification 17 9.5 Summary 119 10 Neural Network Fundamentals 121 10.1 Neural network Basics 21 10.1.1 Introduction to Neural Networks 122 10.1.2 The Perceptron Algorithm 129 10.1.3 Backpropagation and Multi-layer Networks 137 10.1. 4 Multi-layer Networks with Keras 153 10.1.5 The Four Ingredients in a Neural Network Recipe 163 10.1.6 Weight Initialization 165 10.1./ Constant Initialization 165 10.1, 8 Uniform and normal distributions 165 10.19 Le Cun Uniform and norma 166 10.1.10 Glorot/Xavier Uniform and Normal 166 He et al. /Kaiming/MSRA Uniform and Normal 67 10.1. 12 Differences in Initialization Implementation 167 10.2 Summary 168 1 Convolutional Neural Networks 169 1.1 Understanding convolutions I.1 Convolutions versus cross-correlation 170 11.1.2 The Big Matrix and Tiny Matrix" Analogy 171 Il13 Kernels ,171 11.1.4 A Hand Computation Example of Convolution 172 11.1.5 mplementing Convolutions with Python 173 11.1.6 The Role of Convolutions in Deep Learning 11.2 CNN Building Blocks 179 11.2.1 Layer types 81 11.2.2 Convolutional layers 18 11.2.3 Activation Layers 186 11.2. 4 Pooling Layers 186 11.2.5 Fully-connected Layers 188 112.6 Batch normalization ,,,,189 1 1.2./Dropout

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