Practical Machine Learning In Javascript

Download Practical Machine Learning In Javascript full books in PDF, epub, and Kindle. Read online Practical Machine Learning In Javascript ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Practical Machine Learning in JavaScript

Author : Charlie Gerard
Publisher : Apress
ISBN 13 : 9781484264171
Total Pages : 323 pages
Book Rating : 4.2/5 (641 download)

DOWNLOAD NOW!

Book Synopsis Practical Machine Learning in JavaScript by : Charlie Gerard

Download or read book Practical Machine Learning in JavaScript written by Charlie Gerard and published by Apress. This book was released on 2020-11-17 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning web applications without having to learn a new language. This book will help you develop basic knowledge of machine learning concepts and applications. You’ll learn not only theory, but also dive into code samples and example projects with TensorFlow.js. Using these skills and your knowledge as a web developer, you’ll add a whole new field of development to your tool set. This will give you a more concrete understanding of the possibilities offered by machine learning. Discover how ML will impact the future of not just programming in general, but web development specifically. Machine learning is currently one of the most exciting technology fields with the potential to impact industries from health to home automation to retail, and even art. Google has now introduced TensorFlow.js—an iteration of TensorFlow aimed directly at web developers. Practical Machine Learning in JavaScript will help you stay relevant in the tech industry with new tools, trends, and best practices. What You'll Learn Use the JavaScript framework for ML Build machine learning applications for the web Develop dynamic and intelligent web content Who This Book Is For Web developers and who want a hands-on introduction to machine learning in JavaScript. A working knowledge of the JavaScript language is recommended.

Hands-on Machine Learning with JavaScript

Author : Burak Kanber
Publisher : Packt Publishing Ltd
ISBN 13 : 1788990307
Total Pages : 343 pages
Book Rating : 4.7/5 (889 download)

DOWNLOAD NOW!

Book Synopsis Hands-on Machine Learning with JavaScript by : Burak Kanber

Download or read book Hands-on Machine Learning with JavaScript written by Burak Kanber and published by Packt Publishing Ltd. This book was released on 2018-05-29 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: A definitive guide to creating an intelligent web application with the best of machine learning and JavaScript Key Features Solve complex computational problems in browser with JavaScript Teach your browser how to learn from rules using the power of machine learning Understand discoveries on web interface and API in machine learning Book Description In over 20 years of existence, JavaScript has been pushing beyond the boundaries of web evolution with proven existence on servers, embedded devices, Smart TVs, IoT, Smart Cars, and more. Today, with the added advantage of machine learning research and support for JS libraries, JavaScript makes your browsers smarter than ever with the ability to learn patterns and reproduce them to become a part of innovative products and applications. Hands-on Machine Learning with JavaScript presents various avenues of machine learning in a practical and objective way, and helps implement them using the JavaScript language. Predicting behaviors, analyzing feelings, grouping data, and building neural models are some of the skills you will build from this book. You will learn how to train your machine learning models and work with different kinds of data. During this journey, you will come across use cases such as face detection, spam filtering, recommendation systems, character recognition, and more. Moreover, you will learn how to work with deep neural networks and guide your applications to gain insights from data. By the end of this book, you'll have gained hands-on knowledge on evaluating and implementing the right model, along with choosing from different JS libraries, such as NaturalNode, brain, harthur, classifier, and many more to design smarter applications. What you will learn Get an overview of state-of-the-art machine learning Understand the pre-processing of data handling, cleaning, and preparation Learn Mining and Pattern Extraction with JavaScript Build your own model for classification, clustering, and prediction Identify the most appropriate model for each type of problem Apply machine learning techniques to real-world applications Learn how JavaScript can be a powerful language for machine learning Who this book is for This book is for you if you are a JavaScript developer who wants to implement machine learning to make applications smarter, gain insightful information from the data, and enter the field of machine learning without switching to another language. Working knowledge of JavaScript language is expected to get the most out of the book.

Deep Learning with JavaScript

Author : Shanqing Cai
Publisher : Manning Publications
ISBN 13 : 9781617296178
Total Pages : 350 pages
Book Rating : 4.2/5 (961 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning with JavaScript by : Shanqing Cai

Download or read book Deep Learning with JavaScript written by Shanqing Cai and published by Manning Publications. This book was released on 2019-10-07 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Programming Machine Learning

Author : Paolo Perrotta
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680507710
Total Pages : 437 pages
Book Rating : 4.6/5 (85 download)

DOWNLOAD NOW!

Book Synopsis Programming Machine Learning by : Paolo Perrotta

Download or read book Programming Machine Learning written by Paolo Perrotta and published by Pragmatic Bookshelf. This book was released on 2020-03-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Practical Deep Learning for Cloud, Mobile, and Edge

Author : Anirudh Koul
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492034819
Total Pages : 585 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Practical Deep Learning for Cloud, Mobile, and Edge by : Anirudh Koul

Download or read book Practical Deep Learning for Cloud, Mobile, and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. 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. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Practical Machine Learning with H2O

Author : Darren Cook
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491964553
Total Pages : 300 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Practical Machine Learning with H2O by : Darren Cook

Download or read book Practical Machine Learning with H2O written by Darren Cook and published by "O'Reilly Media, Inc.". This book was released on 2016-12-05 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms. If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning. Learn how to import, manipulate, and export data with H2O Explore key machine-learning concepts, such as cross-validation and validation data sets Work with three diverse data sets, including a regression, a multinomial classification, and a binomial classification Use H2O to analyze each sample data set with four supervised machine-learning algorithms Understand how cluster analysis and other unsupervised machine-learning algorithms work

Practical TensorFlow.js

Author : Juan De Dios Santos Rivera
Publisher : Apress
ISBN 13 : 9781484262726
Total Pages : 303 pages
Book Rating : 4.2/5 (627 download)

DOWNLOAD NOW!

Book Synopsis Practical TensorFlow.js by : Juan De Dios Santos Rivera

Download or read book Practical TensorFlow.js written by Juan De Dios Santos Rivera and published by Apress. This book was released on 2020-10-03 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard, ml5js, tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.

Building Data-Driven Applications with Danfo.js

Author : Rising Odegua
Publisher : Packt Publishing Ltd
ISBN 13 : 1801078416
Total Pages : 477 pages
Book Rating : 4.8/5 (1 download)

DOWNLOAD NOW!

Book Synopsis Building Data-Driven Applications with Danfo.js by : Rising Odegua

Download or read book Building Data-Driven Applications with Danfo.js written by Rising Odegua and published by Packt Publishing Ltd. This book was released on 2021-09-24 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniques Key FeaturesBuild microservices to perform data transformation and ML model serving in JavaScriptExplore what Danfo.js is and how it helps with data analysis and data visualizationCombine Danfo.js and TensorFlow.js for machine learningBook Description Most data analysts use Python and pandas for data processing for the convenience and performance these libraries provide. However, JavaScript developers have always wanted to use machine learning in the browser as well. This book focuses on how Danfo.js brings data processing, analysis, and ML tools to JavaScript developers and how to make the most of this library to build data-driven applications. Starting with an overview of modern JavaScript, you'll cover data analysis and transformation with Danfo.js and Dnotebook. The book then shows you how to load different datasets, combine and analyze them by performing operations such as handling missing values and string manipulations. You'll also get to grips with data plotting, visualization, aggregation, and group operations by combining Danfo.js with Plotly. As you advance, you'll create a no-code data analysis and handling system and create-react-app, react-table, react-chart, Draggable.js, and tailwindcss, and understand how to use TensorFlow.js and Danfo.js to build a recommendation system. Finally, you'll build a Twitter analytics dashboard powered by Danfo.js, Next.js, node-nlp, and Twit.js. By the end of this app development book, you'll be able to build and embed data analytics, visualization, and ML capabilities into any JavaScript app in server-side Node.js or the browser. What you will learnPerform data experimentation and analysis with Danfo.js and DnotebookBuild machine learning applications using Danfo.js integrated with TensorFlow.jsConnect Danfo.js with popular database applications to aid data analysisCreate a no-code data analysis and handling system using internal librariesDevelop a recommendation system with Danfo.js and TensorFlow.jsBuild a Twitter analytics dashboard for sentiment analysis and other types of data insightsWho this book is for This book is for data analysts, data scientists, and JavaScript developers who want to create data-driven applications in the JavaScript/Node.js environment. Intermediate-level knowledge of JavaScript programming and data science using pandas is expected.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. 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? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Learning TensorFlow.js

Author : Gant Laborde
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149209076X
Total Pages : 342 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!

Book Synopsis Learning TensorFlow.js by : Gant Laborde

Download or read book Learning TensorFlow.js written by Gant Laborde and published by "O'Reilly Media, Inc.". This book was released on 2021-05-10 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js Use resources to convert, train, and manage machine learning data Build and train your own training models from scratch

Practical Modern JavaScript

Author : Nicolas Bevacqua
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491943548
Total Pages : 334 pages
Book Rating : 4.4/5 (919 download)

DOWNLOAD NOW!

Book Synopsis Practical Modern JavaScript by : Nicolas Bevacqua

Download or read book Practical Modern JavaScript written by Nicolas Bevacqua and published by "O'Reilly Media, Inc.". This book was released on 2017-06-26 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: To get the most out of modern JavaScript, you need learn the latest features of its parent specification, ECMAScript 6 (ES6). This book provides a highly practical look at ES6, without getting lost in the specification or its implementation details. Armed with practical examples, author Nicolas Bevacqua shows you new ways to deal with asynchronous flow control, declare objects or functions, and create proxies or unique sets, among many other features. The first title in Bevacqua’s Modular JavaScript series, Practical Modern JavaScript prepares JavaScript and Node.js developers for applied lessons in modular design, testing, and deployment in subsequent books. This book explains: How JavaScript and its standards development process have evolved Essential ES6 changes, including arrow functions, destructuring, let and const Class syntax for declaring object prototypes, and the new Symbol primitive How to handle flow control with Promises, iterators, generators, and async functions ES6 collection built-in types for creating object maps and unique sets How and when to use the new Proxy and Reflect built-ins Changes to Array, Math, numbers, strings, Unicode, and regular expressions, and other improvements since ES5

Practical Deep Learning

Author : Ronald T. Kneusel
Publisher : No Starch Press
ISBN 13 : 1718500742
Total Pages : 463 pages
Book Rating : 4.7/5 (185 download)

DOWNLOAD NOW!

Book Synopsis Practical Deep Learning by : Ronald T. Kneusel

Download or read book Practical Deep Learning written by Ronald T. Kneusel and published by No Starch Press. This book was released on 2021-02-23 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Practical Artificial Intelligence

Author : Arnaldo Pérez Castaño
Publisher : Apress
ISBN 13 : 1484233573
Total Pages : 701 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis Practical Artificial Intelligence by : Arnaldo Pérez Castaño

Download or read book Practical Artificial Intelligence written by Arnaldo Pérez Castaño and published by Apress. This book was released on 2018-05-23 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses. What You’ll Learn Understand agents and multi agents and how they are incorporated Relate machine learning to real-world problems and see what it means to you Apply supervised and unsupervised learning techniques and methods in the real world Implement reinforcement learning, game programming, simulation, and neural networks Who This Book Is For Computer science students, professionals, and hobbyists interested in AI and its applications.

Practical Machine Learning with Python

Author : Dipanjan Sarkar
Publisher : Apress
ISBN 13 : 1484232070
Total Pages : 545 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis Practical Machine Learning with Python by : Dipanjan Sarkar

Download or read book Practical Machine Learning with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2017-12-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Practical Machine Learning with Rust

Author : Joydeep Bhattacharjee
Publisher : Apress
ISBN 13 : 1484251210
Total Pages : 362 pages
Book Rating : 4.4/5 (842 download)

DOWNLOAD NOW!

Book Synopsis Practical Machine Learning with Rust by : Joydeep Bhattacharjee

Download or read book Practical Machine Learning with Rust written by Joydeep Bhattacharjee and published by Apress. This book was released on 2019-12-10 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Learning Tensorflow. Js

Author : Gant Laborde
Publisher : O'Reilly Media
ISBN 13 : 9781492090793
Total Pages : 300 pages
Book Rating : 4.0/5 (97 download)

DOWNLOAD NOW!

Book Synopsis Learning Tensorflow. Js by : Gant Laborde

Download or read book Learning Tensorflow. Js written by Gant Laborde and published by O'Reilly Media. This book was released on 2021-08-17 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combining the demand for AI with the ubiquity of JavaScript was inevitable. With Google's TensorFlow.js framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on, end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers. You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js. Explore tensors, the most fundamental structure of machine learning Convert data into tensors and back with a real-world example Combine AI with the web using TensorFlow.js and other tools Use resources to convert, train, and manage machine learning data Start building and training your own training models from scratch Learn how to create your own image classification models Examine transfer learning: retraining an advanced model to perform a new task

The Road to Firebase

Author : Robin Wieruch
Publisher : Robin Wieruch
ISBN 13 : 1795010894
Total Pages : 305 pages
Book Rating : 4.7/5 (95 download)

DOWNLOAD NOW!

Book Synopsis The Road to Firebase by : Robin Wieruch

Download or read book The Road to Firebase written by Robin Wieruch and published by Robin Wieruch. This book was released on 2019-01-24 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Road to Firebase is your personal journey to master advanced React for business web applications in JavaScript whereas Firebase is used to replace everything that you would want from a backend application. Firebase enables you to connect your React application to a database, to authenticated users with your application with a login, logout and register mechanisms, and to authorize only certain users to access your application. It also comes with hosting capabilities and with social logins via Google, Facebook and more. Everything will be explained in the book while building a business web application yourself. I wrote the The Road to React with Firebase over the last two years. During this time, I came to understand the practical genius of Firebase, and how it dramatically reduces the tech stack to focus on getting sh*t done. Once you have set up your starter kit project -- that's what we are going to do together in this book -- you are ready to iterate fast on your personal ideas. There is no need to complicate things by adding a backend application with a database to your frontend application, because Firebase takes care of it with a well-designed API. I applied the same principles as my other books: Stay pragmatic Keep it simple Answer the why, not just the how Experience a problem, solve a problem This book is not intended to be an end-all reference for the Firebase API nor an in-depth guide about the internals of Firebase. Instead, its purpose is to journey through learning Firebase with React the pragmatic way, building an entire application on this tech stack yourself. The end result is the foundation to make your business application a reality. Requirements To get the most out of this book, you should be familiar with the basics of web development, which includes knowledge of HTML, CSS and JavaScript. You will also need to be familiar with the term API, because APIs are used frequently for the applications in this book. Editor/Terminal or IDE For the development environment, use a running editor/terminal (command line tool) or IDE with integrated terminal. I will provide a setup guide if you're unsure about which tools to use. The guide is set up for MacOS users, but you can find a Windows setup guide there as well. Node and NPM You will need to have node and npm installed, which are used to run the applications we'll build and manage the libraries we'll use along the way. In this book, you will install external node packages via npm (node package manager). These node packages can be libraries or whole frameworks. You can verify which node and npm versions you have in the command line: node --version v10.11.0 npm --version v6.5.0 These are the versions used for this publication. If you don't see output in your terminal, you will need to install node and npm. React My other book, called The Road to learn React, teaches the fundamentals about React by building a real world application. It is available for free, and after having read it, you should possess all the understanding necessary to work with the application(s) from this book. Also there will be many sidenotes to React articles that may be helpful.