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Hands On Machine Learning With Javascript Pdf


Hands On Machine Learning With Javascript Pdf
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Hands On Machine Learning With Javascript


Hands On Machine Learning With Javascript
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Author : Burak Kanber
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-05-29

Hands On Machine Learning With Javascript written by Burak Kanber and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Computers categories.


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


Deep Learning With Javascript
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Author : Stanley Bileschi
language : en
Publisher: Simon and Schuster
Release Date : 2020-01-24

Deep Learning With Javascript written by Stanley Bileschi and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-24 with Computers categories.


Summary 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. Foreword by Nikhil Thorat and Daniel Smilkov. About the technology Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its 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 fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation. What's inside - Image and language processing in the browser - Tuning ML models with client-side data - Text and image creation with generative deep learning - Source code samples to test and modify About the reader For JavaScript programmers interested in deep learning. About the author Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet. TOC: PART 1 - MOTIVATION AND BASIC CONCEPTS 1 • Deep learning and JavaScript PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS 2 • Getting started: Simple linear regression in TensorFlow.js 3 • Adding nonlinearity: Beyond weighted sums 4 • Recognizing images and sounds using convnets 5 • Transfer learning: Reusing pretrained neural networks PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS 6 • Working with data 7 • Visualizing data and models 8 • Underfitting, overfitting, and the universal workflow of machine learning 9 • Deep learning for sequences and text 10 • Generative deep learning 11 • Basics of deep reinforcement learning PART 4 - SUMMARY AND CLOSING WORDS 12 • Testing, optimizing, and deploying models 13 • Summary, conclusions, and beyond



Hands On Machine Learning With Tensorflow Js


Hands On Machine Learning With Tensorflow Js
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Author : Kai Sasaki
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-11-27

Hands On Machine Learning With Tensorflow Js written by Kai Sasaki and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-27 with Computers categories.


Hands-On Machine Learning with TensorFlow.js is a comprehensive guide that will help you easily get started with machine learning algorithms and techniques using TensorFlow.js. By the end of this book, you will be able to create and optimize your own web-based machine learning applications using practical examples.



Hands On Machine Learning With Tensorflow Js


Hands On Machine Learning With Tensorflow Js
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Author : Kai Sasaki
language : en
Publisher:
Release Date : 2019-11-27

Hands On Machine Learning With Tensorflow Js written by Kai Sasaki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-27 with Computers categories.


Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.js Create smart web applications from scratch with the help of useful examples Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function Book Description TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance. What you will learn Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js Apply the Bellman equation to solve MDP problems Use the k-means algorithm in TensorFlow.js to visualize prediction results Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps Implement tf.js backend frameworks to tune and accelerate app performance Who this book is for This book is for web developers who want to learn how to integrate machine learning techniques with web-based applications from scratch. This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow.js. Working knowledge of JavaScript programming language is all you need to get started.



Practical Machine Learning In Javascript


Practical Machine Learning In Javascript
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Author : Charlie Gerard
language : en
Publisher: Apress
Release Date : 2020-11-17

Practical Machine Learning In Javascript written by Charlie Gerard and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Computers categories.


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.



Learning Tensorflow Js


Learning Tensorflow Js
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Author : Gant Laborde
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-10

Learning Tensorflow Js written by Gant Laborde and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Computers categories.


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



Hands On Artificial Intelligence With Java For Beginners


Hands On Artificial Intelligence With Java For Beginners
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Author : Nisheeth Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31

Hands On Artificial Intelligence With Java For Beginners written by Nisheeth Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.


Build, train, and deploy intelligent applications using Java libraries Key Features Leverage the power of Java libraries to build smart applications Build and train deep learning models for implementing artificial intelligence Learn various algorithms to automate complex tasks Book Description Artificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity. Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach. By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains. What you will learn Leverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among others Build machine learning models using supervised and unsupervised machine learning techniques Implement different deep learning algorithms in Deeplearning4j and build applications based on them Study the basics of heuristic searching and genetic programming Differentiate between syntactic and semantic similarity among texts Perform sentiment analysis for effective decision making with LingPipe Who this book is for Hands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.



Hands On Machine Learning With Scikit Learn Keras And Tensorflow Unsupervised Learning Techniques


Hands On Machine Learning With Scikit Learn Keras And Tensorflow Unsupervised Learning Techniques
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Author : Aurélien Géron
language : en
Publisher:
Release Date : 2019

Hands On Machine Learning With Scikit Learn Keras And Tensorflow Unsupervised Learning Techniques written by Aurélien Géron and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Artificial intelligence categories.


Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.



Hands On Machine Learning With Scikit Learn And Tensorflow


Hands On Machine Learning With Scikit Learn And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-03-13

Hands On Machine Learning With Scikit Learn And Tensorflow written by Aurélien Géron and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-13 with Computers categories.


Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. 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. By using concrete examples, minimal theory, and two production-ready Python frameworks--scikit-learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.



Machine Learning


Machine Learning
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Author : Gabriel Rhys
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-10-18

Machine Learning written by Gabriel Rhys and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-18 with categories.


Can Machines Really Learn?Machine learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning has become an essential pillar of IT in all aspects, even though it has been hidden in the recent past. We are increasingly being surrounded by several machine learning-based apps across a broad spectrum of industries. From search engines to anti-spam filters to credit card fraud detection systems, list of machine learning applications is ever-expanding in scope and applications. The goal of this book is to provide you with a hands-on, project-based overview of machine learning systems and how they are applied over a vast spectrum of applications that underpins AI technology from Absolute Beginners to Experts.This book is a fast-paced, thorough introduction to Machine Learning that will have you writing programs, solving problems, and making things that work in no time.This book presents algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including: Supervised and Unsupervised learning methods Artificial Neural Networks Hands-on projects based on Real-world applications Bayesian learning method Reinforcement learning And much more By the end of this book, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning. Learning Outcomes: By the end of this book, you will be able to: Identify potential applications of machine learning in practice Describe the core differences in analyses enabled by regression, classification, and clustering Select the appropriate machine learning task for a potential application Apply regression, classification, and clustering Represent your data as features to serve as input to machine learning models Utilize a dataset to fit a model to analyze new data Build an end-to-end application that uses machine learning at its core Implement these techniques in Python If you've been thinking seriously about digging into ML, this book will get you up to speed. Why wait any longer?