Deep Learning in Practice

Description

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step. Cover Half Title Title Page Copyright Page Table of Contents Preface Acknowledgments Author Chapter 1: Introduction 1.1 What Is Learning? 1.2 What Is Machine Learning? 1.3 What Is Deep Learning? 1.4 About This Book! 1.4.1 Introduction 1.4.2 Python/NumPy 1.4.3 TensorFlow and Keras Fundamentals 1.4.4 Artificial Neural Networks (ANNs) Fundamentals and Architectures 1.4.5 Deep Neural Networks (DNNs) Fundamentals and Architectures 1.4.6 Deep Neural Networks for Images and Audio Data Analysis 1.4.7 Deep Neural Networks for Virtual Assistant Robots 1.4.8 Finding the Best Model? Chapter 2: Python/NumPy Fundamentals 2.1 Python 2.1.1 Variables 2.1.2 Keywords 2.1.3 Operators and Operand 2.1.4 Statements and Expressions 2.1.5 Sequence 2.1.6 For Loop 2.1.7 While Loop 2.1.8 String 2.1.9 List 2.1.10 Dictionary 2.1.11 Tuple 2.1.12 Sets 2.1.13 Function 2.1.14 File 2.1.15 Object (class) 2.2 NumPy 2.2.1 Create Array 2.2.2 ndarray 2.2.3 Access Elements 2.2.4 Array Slicing 2.2.5 Data Type 2.2.6 Array Data Check 2.2.7 Shape and Reshape Array 2.2.8 Array Iterating 2.2.9 Joining Array 2.2.10 Splitting Array 2.2.11 Searching Arrays 2.2.12 Sorting Arrays 2.2.13 Filter Array 2.2.14 Random Numbers 2.2.15 Array Vectorization 2.2.16 np.zeros and np.ones 2.2.17 hstack and vstack 2.2.18 Generate Random Numbers 2.2.19 Mathematical Functions 2.2.20 Dot Product and Matrix Multiplication 2.2.21 Determinant Chapter 3: TensorFlow and Keras Fundamentals 3.1 How does TensorFlow Work? 3.2 Tensors 3.3 TensorFlow 3.4 Building an NN Using TensorFlow 3.4.1 Import the Data 3.4.2 Load and Normalize the Data 3.4.3 Build the Model 3.4.4 Train and Evaluate the Model 3.5 Building a CNN Using TensorFlow 3.5.1 Dataset 3.5.2 Input Layer 3.5.3 Convolutional and Pooling Layers 3.5.4 Dense Layer 3.5.5 Train and Evaluate the Model 3.5.6 Test the Model 3.6 Setup and Install Keras 3.6.1 Create a Virtual Environment 3.6.2 Activate the Environment 3.6.3 Python Libraries 3.6.4 Available Modules 3.6.5 Import Libraries and Modules 3.6.6 Train and Predict the Model 3.7 Implement an Example Using Keras 3.7.1 MNIST Example Chapter 4: Artificial Neural Networks (ANNs) Fundamentals and Architectures 4.1 Terminology 4.1.1 Inputs 4.1.2 Weights 4.1.3 Outputs 4.1.4 Targets 4.1.5 Activation Function 4.1.6 Error 4.1.7 Training, Testing, and Validation Sets 4.1.8 Overfitting 4.1.9 Underfitting 4.1.10 Confusion Matrix 4.1.11 Accuracy Metrics 4.1.12 Balanced and Unbalanced Datasets 4.1.13 One Hot Encoding 4.2 Artificial Neural Networks (ANNs) 4.2.1 Biological Neuron 4.2.2 Artificial Neuron 4.3 Activation Functions 4.3.1 Sigmoid (sig) 4.3.2 Tanh or Hyperbolic Tangent (tan) 4.3.3 Rectified Linear Unit (ReLU) 4.3.4 Leaky ReLU 4.3.5 Softmax 4.4 Loss Function 4.4.1 Cross-Entropy Loss 4.4.2 MSE (L 2) Loss 4.5 Optimization Functions 4.5.1 Learning Rate 4.5.2 Convex 4.5.3 Gradient Descent 4.5.4 Stochastic Gradient Descent 4.5.5 Adagrad 4.5.6 Adam 4.6 Linear and Nonlinear Functions 4.6.1 Linear Function 4.6.2 Nonlinear Functions 4.7 ANNs Architectures 4.7.1 Feed Forward Neural Networks (FFNNs) 4.7.1.1 FFN Example with TensorFlow 4.7.2 Backpropagation 4.7.3 Single-Layer Perceptron 4.7.4 Multi-Layer Perceptron (MLP) 4.7.4.1 MLP Example in TensorFlow Chapter 5: Deep Neural Networks (DNNs) Fundamentals and Architectures 5.1 Deep Neural Networks 5.1.1 What, Is Deep Learning? 5.1.2 Deep Learning Needs! 5.1.3 How to Deploy DL More Efficiently? 5.1.4 Vanishing Gradient 5.1.5 Channel 5.1.6 Embedding 5.1.7 Fine-Tuning 5.1.8 Data Augmentation 5.1.9 Generalization 5.1.10 Regularization 5.1.11 L 1 and L 2 5.1.12 Dropout 5.1.13 End-to-End Learning 5.2 Deep Learning Applications 5.3 Deep Learning Algorithms and Architectures 5.3.1 Convolutional Neural Networks (CNNs) 5.3.2 Recurrent Neural Networks (RNNs) 5.3.3 Long Short-Term Memory (LSTM) 5.3.4 Generative Adversarial Networks (GANs) 5.3.5 Residual Neural Network Learning (ResNets) 5.4 Convolutional Neural Networks (CNNs) 5.4.1 CNN Layers 5.4.1.1 Convolution Layers 5.4.1.2 Pooling Layers 5.4.1.3 Dropout 5.4.1.4 Batch Normalization 5.4.1.5 Fully Connected Layer 5.4.2 Design a CNN 5.5 Recurrent Neural Networks (RNNs) 5.5.1 Recurrent Neural Network Architecture 5.5.2 Long Short-Term Memory (LSTM) 5.5.3 Designing an RNN (LSTM) 5.5.3.1 Import Libraries 5.5.3.2 Load and Normalize the Dataset 5.5.3.3 Build the Model 5.5.3.4 Train the Model 5.5.3.5 Evaluate the Model 5.6 Generative Adversarial Network (GANs) 5.6.1 What is a GAN? 5.6.2 A GAN for Fashion Dataset 5.6.2.1 Loading Dataset 5.6.2.2 Data Preprocessing 5.6.2.3 Defining the Discriminator Model 5.6.2.4 Defining the Generator Model 5.6.2.5 Combining the Generator and Discriminator Model 5.6.2.6 Create Train Function and Train the Model 5.6.2.7 Predict (Generate Data) Chapter 6: Deep Neural Networks (DNNs) for Images Analysis 6.1 Deep Learning and Image Analysis 6.2 Convolutional Neural Networks (CNNs) and Image Analysis 6.2.1 Filter Parameters 6.2.1.1 Number and Type of the Filters 6.2.1.2 Filters Size 6.2.1.3 Stride and Padding Size 6.2.2 Number of Parameters 6.2.2.1 Input Layer 6.2.2.2 Convolutional Layer 6.2.2.3 Pooling Layer 6.2.2.4 Fully Connected Layer (FC) 6.2.3 Imagenet Challenge 6.2.4 CNN Architecture 6.2.4.1 LeNet-5 (1998) 6.2.4.2 AlexNet (2012) 6.2.4.3 GoogleNet/Inception-v1 (2014) 6.2.4.4 VGGNet-16 (2014) 6.2.4.5 Inception-v3 (2015) 6.2.4.6 ResNet (2015) 6.2.4.7 Inception-v4 (2016) 6.3 General Strategy to Implement Model Using CNNs 6.3.1 Import Libraries 6.3.2 Load the Data and Create the Data Categories 6.3.3 Make the Model 6.3.4 Train the Model 6.3.5 Test the Model 6.4 Object Recognition Using CNNs 6.4.1 Import Libraries 6.4.2 Load the Data and Generate a Dataset 6.4.3 Make the Model 6.4.4 Train the Model 6.4.5 Test the Model 6.5 Image Classification Using CNNs 6.5.1 Import Libraries 6.5.2 Load the Data 6.5.3 Make the Model 6.5.4 Train the Model 6.5.5 Test the Model 6.6 Image Segmentation 6.6.1 Import Libraries 6.6.2 Load the Data and Generate a Dataset 6.6.3 Segmentation Map 6.6.4 Make the Model 6.6.5 Train the Model 6.6.6 Test the Model 6.7 Object Recognition Using CNNs 6.7.1 Import Libraries 6.7.2 Load the Data and Generate a Dataset 6.7.3 Make the Model 6.7.3.1 The Generator Function 6.7.3.2 The Discriminator Function 6.7.4 Train the Model 6.7.5 Generate Images Chapter 7: Deep Neural Networks (DNNs) for Virtual Assistant Robots 7.1 Virtual Assistant Robot 7.2 Facial Detection and Recognition 7.2.1 Architecture 7.2.2 Face Detection 7.2.2.1 Import Libraries 7.2.2.2 Dataset 7.2.2.3 Define CNN Model and Training 7.2.2.4 Model Training 7.2.2.5 Evaluate Performance 7.2.3 Landmark Detection 7.2.3.1 CNN Model 7.2.3.2 Model Training 7.2.3.3 Test the Trained Model 7.2.4 Spoof Detection 7.2.5 Encoding the Face 7.2.6 Training 7.2.7 Testing 7.3 Emotion Recognition Using Speech 7.3.1 Dataset Collection 7.3.2 Data Preprocessing 7.3.2.1 Labeling 7.3.3 Feature Extraction 7.3.3.1 Data Augmentation 7.3.4 Model Training 7.3.5 Model Evaluation 7.3.6 Test the Trained Model 7.4 Speech to Text 7.4.1 Feature Extraction 7.4.2 Deep Neural Networks Modeling 7.4.3 Decoder 7.4.4 Predictions Calculation 7.5 Sentiment Analysis 7.5.1 Load Dataset 7.5.2 Create a DAN Network 7.5.3 Train the Network 7.5.4 Evaluate the Model Chapter 8: Finding the Best Model 8.1 Data Preprocessing 8.2 What Is a Good Model? 8.3 What Is the Noise? 8.3.1 Labeling 8.3.2 Features 8.4 What Is the Bias? 8.4.1 Incorrect Classifier 8.4.2 Incorrect Features 8.5 What Is the Variance? 8.5.1 New Dataset 8.6 Bias/Variance 8.7 How Can We Find the Problems in a Model? 8.7.1 High Variance 8.7.2 High Bias Bibliography Websites Index