Machine Learning For Beginners An Introduction For Beginners Why Machine Learning Matters Today And How Machine Learning Networks Algorithms Concepts And Neural Networks Really Work


Machine Learning For Beginners An Introduction For Beginners Why Machine Learning Matters Today And How Machine Learning Networks Algorithms Concepts And Neural Networks Really Work
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Machine Learning For Beginners


Machine Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-09-07

Machine Learning For Beginners written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-07 with Computers categories.


If you are looking for a complete beginners guide to learn machine learning with examples, in just a few hours, then you need to continue reading. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. ★★ Grab your copy today and learn ★★ ♦ The different types of learning algorithm that you can expect to encounter ♦ The numerous applications of machine learning ♦ The different types of machine learning and how they differ ♦ The best practices for picking up machine learning ♦ What languages and libraries to work with ♦ The future of machine learning ♦ The various problems that you can solve with machine learning algorithms ♦ And much more... Starting from nothing, we slowly work our way through all the concepts that are central to machine learning. By the end of this book, you're going to feel as though you have an extremely firm understanding of what machine learning is, how it can be used, and most importantly, how it can change the world. You're also going to have an understanding of the logic behind the algorithms and what they aim to accomplish. Don't waste your time working with a book that's only going to make an already complicated topic even more complicated. Scroll up and click the buy now button to learn everything you need to know about Machine Learning!



Machine Learning For Beginners


Machine Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-09-07

Machine Learning For Beginners written by Steven Cooper 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 2018-09-07 with categories.


Buy the Paperback version of this bundle, and get the Kindle eBook version included for FREE Get 2 books in 1! This is the bundle of two successful books in the market! Book 1: In "Machine Learning: An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work" you will learn: The different types of learning algorithm that you can expect to encounter The numerous applications of machine learning The different types of machine learning and how they diffe The best practices for picking up machine learning What languages and libraries to work with The future of machine learning The various problems that you can solve with machine learning algorithms And much more... Book 2: In "Neural Networks: A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention" you will learn: The history of neural networks and the way modern neural networks work How deep learning REALLY works The different types of neural networks The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST How to build your own neural network An effective technique for hacking into a neural network Some introductory advice for modifying parameters in the code-based environment And much more... These books provide proven concepts and strategies for people who want to know more about machine learning and neural networks. After reading this bundle you will be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. Get your copy of these fantastic guides as a part of your commitment to improving your data knowledge. Don't wait any longer and scroll up to click the buy now button!



Deep Learning For Beginners


Deep Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-09-06

Deep Learning For Beginners written by Steven Cooper 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 2018-09-06 with categories.


Buy the Paperback version of this bundle, and get the Kindle eBook version included for FREE Get 2 books in 1! This is the bundle of two successful books in the market! Book 1: In "Machine Learning: An Introduction for Beginners, Why Machine Learning Matters Today and How Machine Learning Networks, Algorithms, Concepts and Neural Networks Really Work" you will learn: The different types of learning algorithm that you can expect to encounter The numerous applications of machine learning The different types of machine learning and how they diffe The best practices for picking up machine learning What languages and libraries to work with The future of machine learning The various problems that you can solve with machine learning algorithms And much more... Book 2: In "Neural Networks: A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention" you will learn: The history of neural networks and the way modern neural networks work How deep learning REALLY works The different types of neural networks The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST How to build your own neural network An effective technique for hacking into a neural network Some introductory advice for modifying parameters in the code-based environment And much more... These books provide proven concepts and strategies for people who want to know more about machine learning and neural networks. After reading this bundle you will be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. Get your copy of these fantastic guides as a part of your commitment to improving your data knowledge. Don't wait any longer and scroll up to click the buy now button!



Machine Learning For Dummies


Machine Learning For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2016-05-31

Machine Learning For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-31 with Computers categories.


Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!



Deep Learning


Deep Learning
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Author : Ian Goodfellow
language : en
Publisher: MIT Press
Release Date : 2016-11-10

Deep Learning written by Ian Goodfellow and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-10 with Computers categories.


An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. 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. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.



How Smart Machines Think


How Smart Machines Think
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Author : Sean Gerrish
language : en
Publisher: MIT Press
Release Date : 2019-10-22

How Smart Machines Think written by Sean Gerrish and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-22 with Computers categories.


Everything you want to know about the breakthroughs in AI technology, machine learning, and deep learning—as seen in self-driving cars, Netflix recommendations, and more. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM’s Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today’s machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson’s famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people.



Grokking Deep Learning


Grokking Deep Learning
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Author : Andrew W. Trask
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-23

Grokking Deep Learning written by Andrew W. Trask 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 2019-01-23 with Computers categories.


Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide



A I Artificial Intelligence Introduction To The Issues And Current Trends


A I Artificial Intelligence Introduction To The Issues And Current Trends
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Author : Pavel Bartoš
language : en
Publisher: Evropská akademie vzdělávání SE (European Academy of education)
Release Date : 2023-01-01

A I Artificial Intelligence Introduction To The Issues And Current Trends written by Pavel Bartoš and has been published by Evropská akademie vzdělávání SE (European Academy of education) this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-01 with Computers categories.


Artificial Intelligence (AI), often referred to by the acronym Artificial Intelligence, is a fascinating and dynamically developing field that has far-reaching implications for various sectors of human activity. At its core, it is the simulation of human intelligence in computer systems. AI is capable of learning, planning, problem solving, speech recognition, visual perception and many other cognitive functions that were previously considered the exclusive domain of human thought.



Deep Learning For Beginners


Deep Learning For Beginners
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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-11-06

Deep Learning For Beginners written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-06 with Computers categories.


☆★The Best Deep Learning Book for Beginners★☆ If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading. This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley. This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible. ★★ Grab your copy today and learn ★★ ♦ Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google. ♦ The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems. ♦ The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it. ♦ The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for the integration of various systems via an artificial intelligence system, which is already being used for the home and car functions. ♦ And much more... The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow. This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch. When data science can reduce spending costs by billions of dollars in the healthcare industry, why wait to jump in? If you want to get started on deep learning and the concepts that run artificial technologies, don't wait any longer. Scroll up and click the buy now button to get this book today!



Mathematics For Machine Learning


Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23

Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.


Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.