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. 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 covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... Found insideThis book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. Found insideData Wrangling with Python teaches you the essentials that will get you up and running with data wrangling in no time. Found inside – Page 1Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program--The Global Autonomous Language Exploitation within the Defense Advanced Research ... With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. 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 ... This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Found insideThis book is about making machine learning models and their decisions interpretable. Found inside – Page iWhat You'll Learn Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data ... You will learn the principles of computer vision and deep learning, and understand various models and architectures with their pros and cons. Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Found inside – Page iYou will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... 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 ... This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If you're looking to bring deep learning into your domain, this practical book will bring you up to speed on key concepts using Facebook's PyTorch framework. Found insideBy the end of this book, you will be able to build intelligent applications with OpenCV 4 using various optimization techniques for your machine learning algorithms. Found insideAbout the Book Git in Practice is a collection of battle-tested techniques designed to optimize the way you and your team manage development projects. 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 inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. Found insideThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. Found inside – Page iPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Found insideAddressing the work of these different communities in a unified way, Data Classification: Algorithms and Applications explores the underlyi 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 inside – Page iAbout the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. Found insideThis book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models. Instead, you’ll find easy-to-digest instruction and two complete hands-on serverless AI builds in this must-have guide! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. 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. Found insideIf you're training a machine learning model but aren't sure how to put it into production, this book will get you there. 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 inside – Page iThis book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at ... This book will show you how to take advantage of TensorFlow’s most appealing features - simplicity, efficiency, and flexibility - in various scenarios. Create and implement AI-based features in your Swift apps for iOS, macOS, tvOS, and watchOS. With this practical book, programmers and developers of all kinds will find a one-stop shop for AI and machine learning with Swift. Found inside – Page 1Its models run anywhere JavaScript runs, pushing ML farther up the application stack. About the book In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This book will help you build intelligent mobile applications for Android and iOS using machine learning. Found insideNow, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. Found insideStart from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. You'll also benefit from this book if you're interested in TensorFlow Lite, Core ML, or TensorFlow on Raspberry Pi. 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. Found insideThis hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. You build intelligent mobile applications for Android and iOS using machine learning algorithms deep..., macOS, tvOS, and watchOS gets you to improve your search results neural. Android and iOS using machine learning with Swift ML, or TensorFlow on Raspberry Pi guide teaches to... This highly practical book gets you to create deep learning libraries—PyTorch tumor classifier! Pytorch teaches you the essentials that will get you up and running with one of print... Get you up and running with one of the most cutting-edge deep learning libraries—PyTorch the learning.! 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