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Deep Learning Essentials - Comprehensive Guide for Beginners & Professionals | AI, Machine Learning & Neural Networks | Perfect for Students, Developers & Data Scientists
Deep Learning Essentials - Comprehensive Guide for Beginners & Professionals | AI, Machine Learning & Neural Networks | Perfect for Students, Developers & Data Scientists

Deep Learning Essentials - Comprehensive Guide for Beginners & Professionals | AI, Machine Learning & Neural Networks | Perfect for Students, Developers & Data Scientists

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Description

Get to grips with the essentials of deep learning by leveraging the power of PythonKey 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 DescriptionDeep 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 ForAspiring 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

Reviews

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This is a great introduction to DL. Covers all the bases. Lots of tips, tricks and applications.One drawback is that nothing is covered in enough detail to truly implement a production system.Based on the level of detail, I would give this book three stars. Based on the content, I would give this book 5 stars. My rating averages the score for the content and coverage.There doesn’t appear to be a single book that covers everything in enough detail to implement a production system for both natural language processing and computer vision systems.The ImageNet bundle from pyimagesearch has everything you need to build a deep learning system for computer vision applications but the cost could be prohibitive for some. OTOH a box with four GTX 1080 TIs, 128Gb ram, a decent CPU, and the rest of the parts could easily cost $6,000. $600 for books and code is just another 10% relative to the cost of a DL box.This won’t be the only DL book you read. I’ve read most of the books on the market. My favorites include Chollet’s Keras book and Geron’s Tensorflow book as well as Adrian Rosebrock’s books which use Keras and MXNet. This one is a very useful addition to my library.You will need to read this book and at least two of Chollet, Geron, and Rosebrock to have a reasonable grasp of the important concepts for DL for computer vision. Arxiv-sanity preserver is a great way to keep up to date on the research literature.It would be helpful to have a working knowledge of Docker as well.I’m looking forward to finding a good book on PyTorch to complement my knowledge of DL frameworks.