Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling (English Edition)

Description

Get to grips with the essentials of deep learning by leveraging the power of Python Key FeaturesYour one-stop solution to get started with the essentials of deep learning and neural network modelingTrain different kinds of neural networks to tackle various problems in Natural Language Processing, computer vision, and speech recognitionCover popular Python libraries such as TensorFlow and Keras, along with tips on training, deploying, and optimizing your deep learning models in the best possible mannerBook Description Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as Convolutional Neural Network and Recurrent Neural Network, and will see some of their applications in real-world domains including computer vision, natural language processing, and speech recognition. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing. Popular Python library such as TensorFlow is used in this book to build the models. This book also covers solutions for different problems you might come across while training models, such as noisy datasets and small datasets By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications. What you will learnGet to grips with the core concepts of deep learning and neural networksSet up deep learning library such as TensorFlowFine-tune your deep learning models for NLP and computer vision applicationsUnify different information sources, such as images, text, and speech through deep learningOptimize and fine-tune your deep learning models for better performanceTrain a deep reinforcement learning model that plays a game better than humansLearn how to make your models get the best out of your GPU or CPUWho This Book Is For Aspiring data scientists and machine learning experts who have limited or no exposure to deep learning will find this book to be very useful. If you are looking for a resource that gets you up and running with the fundamentals of deep learning and neural networks, this book is for you. As the models in the book are trained using the popular Python-based libraries such as TensorFlow and Keras, it would be useful to have sound programming knowledge of Python. Prior knowledge of deep learning is not required. Table of ContentsWhy Deep Learning?Getting Yourself Ready for Deep LearningGetting Started with Neural NetworksDeep learning in Computer VisionNatural language processing - Vector RepresentationAdvanced Natural language processingMulti-modalityReinforcement LearningDeep Learning HacksDeep Learning Trends Cover Title Page Copyright and Credits Packt Upsell Contributors Table of Contents Preface Chapter 1: Why Deep Learning? What is AI and deep learning? The history and rise of deep learning Why deep learning? Advantages over traditional shallow methods Impact of deep learning The motivation of deep architecture The neural viewpoint The representation viewpoint Distributed feature representation Hierarchical feature representation Applications Lucrative applications Success stories Deep learning for business Future potential and challenges Summary Chapter 2: Getting Yourself Ready for Deep Learning Basics of linear algebra Data representation Data operations Matrix properties Deep learning with GPU Deep learning hardware guide CPU cores RAM size Hard drive Cooling systems Deep learning software frameworks TensorFlow – a deep learning library Caffe MXNet Torch Theano Microsoft Cognitive Toolkit Keras Framework comparison Setting up deep learning on AWS Setup from scratch Setup using Docker Summary Chapter 3: Getting Started with Neural Networks Multilayer perceptrons The input layer The output layer Hidden layers Activation functions Sigmoid or logistic function Tanh or hyperbolic tangent function ReLU Leaky ReLU and maxout Softmax Choosing the right activation function How a network learns Weight initialization Forward propagation Backpropagation Calculating errors Backpropagation Updating the network Automatic differentiation Vanishing and exploding gradients Optimization algorithms Regularization Deep learning models Convolutional Neural Networks Convolution Pooling/subsampling Fully connected layer Overall Restricted Boltzmann Machines Energy function Encoding and decoding Contrastive divergence (CD-k) Stacked/continuous RBM RBM versus Boltzmann Machines Recurrent neural networks (RNN/LSTM) Cells in RNN and unrolling Backpropagation through time Vanishing gradient and LTSM Cells and gates in LTSM Step 1 – The forget gate Step 2 – Updating memory/cell state Step 3 – The output gate Practical examples TensorFlow setup and key concepts Handwritten digits recognition Summary Chapter 4: Deep Learning in Computer Vision Origins of CNNs Convolutional Neural Networks  Data transformations Input preprocessing Data augmentation Network layers Convolution layer Pooling or subsampling layer Fully connected or dense layer Network initialization Regularization Loss functions Model visualization Handwritten digit classification example Fine-tuning CNNs Popular CNN architectures AlexNet Visual Geometry Group GoogLeNet ResNet Summary Chapter 5: NLP - Vector Representation Traditional NLP Bag of words Weighting the terms tf-idf Deep learning NLP Motivation and distributed representation Word embeddings Idea of word embeddings Advantages of distributed representation Problems of distributed representation Commonly used pre-trained word embeddings Word2Vec Basic idea of Word2Vec The word windows Generating training data Negative sampling Hierarchical softmax Other hyperparameters Skip-Gram model The input layer The hidden layer The output layer The loss function Continuous Bag-of-Words model Training a Word2Vec using TensorFlow Using existing pre-trained Word2Vec embeddings Word2Vec from Google News Using the pre-trained Word2Vec embeddings Understanding GloVe FastText Applications Example use cases Fine-tuning Summary Chapter 6: Advanced Natural Language Processing Deep learning for text Limitations of neural networks Recurrent neural networks  RNN architectures Basic RNN model Training RNN is tough Long short-term memory network LSTM implementation with tensorflow Applications Language modeling Sequence tagging Machine translation Seq2Seq inference Chatbots Summary Chapter 7: Multimodality What is multimodality learning? Challenges of multimodality learning Representation Translation Alignment Fusion Co-learning Image captioning Show and tell Encoder Decoder Training Testing/inference Beam Search Other types of approaches Datasets Evaluation BLEU ROUGE METEOR CIDEr SPICE Rank position Attention models Attention in NLP Attention in computer vision The difference between hard attention and soft attention Visual question answering Multi-source based self-driving Summary Chapter 8: Deep Reinforcement Learning What is reinforcement learning (RL)?  Problem setup Value learning-based algorithms Policy search-based algorithms Actor-critic-based algorithms Deep reinforcement learning Deep Q-network (DQN) Experience replay Target network Reward clipping Double-DQN Prioritized experience delay Dueling DQN Implementing reinforcement learning Simple reinforcement learning example Reinforcement learning with Q-learning example Summary Chapter 9: Deep Learning Hacks Massaging your data Data cleaning Data augmentation Data normalization Tricks in training Weight initialization All-zero Random initialization ReLU initialization Xavier initialization Optimization Learning rate Mini-batch Clip gradients Choosing the loss function Multi-class classification Multi-class multi-label classification Regression Others Preventing overfitting Batch normalization Dropout Early stopping Fine-tuning Fine-tuning When to use fine-tuning When not to use fine-tuning Tricks and techniques Model compression Summary Chapter 10: Deep Learning Trends Recent models for deep learning Generative Adversarial Networks  Capsule networks  Novel applications Genomics Predictive medicine Clinical imaging Lip reading Visual reasoning Code synthesis Summary Other Books You May Enjoy Index