Found inside – Page iiThis book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. Found insideThis book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy ... Found insideIn this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. If you’re a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library. In the past few years, we’ve seen many data products based on predictive modeling. Found inside – Page iAbout the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. Found insideDeep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Found insideThis hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Found inside – Page iThis book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. With this practical guide, Andreas M. Antonopoulos and Gavin Wood provide everything you need to know about building smart contracts and DApps on Ethereum and other virtual-machine blockchains. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts. This book will show you how. About the Book Deep Learning for Search teaches you to improve your search results with neural networks. You'll review how DL relates to search basics like indexing and ranking. Found insidePython Machine Learning will help coders of all levels master one of the most in-demand programming skillsets in use today. Found inside – Page 1But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? Found insideEvery chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. Found insideWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial ... Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset. How does the computer learn to understand what it sees? Deep Learning for Vision Systems answers that by applying deep learning to computer vision. Using only high school algebra, this book illuminates the concepts behind visual intuition. Found insideThis practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. The book will help you learn deep neural networks and their applications in computer vision, generative models, and natural language processing. Found insideThis book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. Found insideWhat you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum ... This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using ... Found insideThis book includes a selection of articles from The 2019 World Conference on Information Systems and Technologies (WorldCIST’19), held from April 16 to 19, at La Toja, Spain. Found insideIt is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. This beginner-friendly guide will give you hands-on experience: learning PyTorch basics developing your first PyTorch neural network exploring neural network refinements to improve performance introduce CUDA GPU acceleration It will ... Found insideStyle and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. What You Will Learn Create advanced data visualizations via R using the ggplot2 package Ingest data using R and Python to overcome some limitations of Power Query Apply machine learning models to your data using R and Python without the ... Found insideThis book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. 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