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Programming Machine Learning : From Coding to Deep Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start 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.Apart from that, you'll only need your computer, and your code-adept brain.
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Learning, Training, and Development in Organizations
This scholarly book in SIOP’s Organizational Frontier series looks at research on enhancing knowledge acquisition and its application in organizations.It concentrates on training, design and delivery given the changing nature of work and organizations.Now that work is increasingly complex, there is greater emphasis on expertise and cognitive skills.Advances in technology such as computer simulations and web-based training are necessitating a more active role for the learner in the training process.In the broad context of the organization systems, this book promotes learning and development as a continuous lifelong endeavor.
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Organizers in Development : Volume 157
Organizers in Development, Volume 157 in the Current Topics in Developmental Biology series, highlights new advances in the field, with this new volume presenting interesting chapters on topics such as The organizer: what it meant, and means, to developmental biology, Organizers/signaling regions that pattern the limb, Organizers/signaling regions in the branchial arches, The notochord/floor plate as a possible organizer, Transport and gradient formation of Wnt and Fgf in the early zebrafish gastrula, and more.
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Effective Software Development for the Enterprise : Beyond Domain Driven Design, Software Architecture, and Extreme Programming
It’s a jungle out there. Delivering software solutions with predictable costs, time, scope, and quality has become more complicated than ever.Developers need to account for multiple moving targets, including often avoided but always relevant factors such as culture, people, and leadership.To effectively deal with these challenges, it’s necessary to rethink the entire delivery chain of modern software development.This book addresses all these topics, putting everything in perspective, from the potential awkwardness created by cultural differences to connecting code with business outcomes to make delivering quality solutions possible.As a software engineer who strives to be his best and challenge the status quo when building IT systems, author Tengiz Tutisani always wanted a book that would cover all of these elements from a developer’s perspective — so he wrote one.Effective Software Development for the Enterprise builds on advanced disciplines, methodologies, and techniques derived from domain-driven design, software architecture, and extreme programming.Its objective is to aid modern software engineers and solution architects in building and delivering high-quality solutions for an enterprise while meeting ambitious criteria:Meet users' expectationsDeliver solutions on time with no defectsCreate products that can scale-out horizontallySolutions should not require a dedicated production support teamAccelerate development paceDouble ROI per developer, team, and softwareYou will learn how to achieve these objectives in any engineering environment by developing solid strategies based on the industry's well-known, recognized, and proven patterns and principles.After reading this book, you will be ready to effectively deal with any and all challenges posed by today’s hyper-competitive, globally-diverse, fast-moving enterprise environment. Who This Book Is ForProfessional software engineers and solution architects, as well as those aspiring to become one.Additionally, software engineering leaders will benefit from reading it, as it will help them maximize their impact via successful software project deliveries.
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Heart Development and Disease : Volume 156
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Inference and Learning from Data: Volume 3 : Learning
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks.A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code.Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
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Java Programming Exercises : Volume Two: Java Standard Library
Take the next step in raising your coding skills and dive into the intricacies of Java Standard Libraries.You will continue to raise your coding skills, and test your Java knowledge on tricky programming tasks, with the help of the pirate Captain CiaoCiao.This is the second of two volumes which provide you with everything you need to excel in your Java journey, including tricks that you should know in detail as a professional, as well as intensive training for clean code and thoughtful design that carries even complex software. Features:149 tasks with commented solutions on different levelsFor all paradigms: object-oriented, imperative, and functionalClean code, reading foreign code, and object-oriented modelingWith numerous best practices and extensively commented solutions to the tasks, these books provide the perfect workout for professional software development with Java.
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Learning Professional Python : Volume 2: Advanced
Volume 2 of Learning Professional Python is a resource for students who want to learn Python even if they don’t have any programming knowledge and for teachers who want a comprehensive introduction to Python to use with their students.This book helps the students achieve their dream job in the IT Industry and teaches the students in an easy, understandable manner while strengthening coding skills. Learning Professional Python: Volume 2 Objectives Become familiar with the features of Python programming language Introduce the object-oriented programming concepts Discover how to write Python code by following the object-oriented programming concepts Become comfortable with concepts such as classes, objects, inheritance, dynamic dispatch, interfaces, and packages Learn the Python generics and collections Develop exception handling and the multithreaded applications Design graphical user interface (GUI) applications
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