Ai Machine Learning A Complete Guide 2019 Edition
Download Ai Machine Learning A Complete Guide 2019 Edition full books in PDF, epub, and Kindle. Read online free Ai Machine Learning A Complete Guide 2019 Edition ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Neil Wilkins |
| Publisher | : Independently Published |
| Total Pages | : 214 |
| Release | : 2019-04-06 |
| Genre | : |
| ISBN | : 9781092879675 |
If you want to learn key AI concepts to get you quickly up to speed with all things AI, then keep reading Two manuscripts in one book: Artificial Intelligence: What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future Internet of Things: What You Need to Know About IoT, Big Data, Predictive Analytics, Artificial Intelligence, Machine Learning, Cybersecurity, Business Intelligence, Augmented Reality and Our Future This book covers everything from machine learning to robotics and the internet of things. You can use it as a nifty guidebook whenever you come across news headlines that talk about some new advancement in AI by Google or Facebook. By the time you finish reading, you will be aware of what artificial neural networks are, how gradient descent and back propagation work, and what deep learning is. You will also learn a comprehensive history of AI, from the first invention of automations in antiquity to the driver-less cars of today. In part 1 of this book, you will: Understand how machines can "think" and how they learn Learn the five reasons why experts are warning us about AI research Find the answers to the top six myths of artificial intelligence Learn what neural networks are and how they work, the "brains" of machine learning Understand reinforcement learning and how it is used to teach machine learning systems through experience Become up-to-date with the current state-of-the-art artificial intelligence methods that use deep learning Learn the basics of recommender systems Expand your current view of machines and what is possible with modern robotics Enter the vast world of the internet of things technologies Find out why AI is the new business degree And much, much more! Some of the topics covered in part 2 of this book include: Origins of IoT IoT Security Ethical Hacking Internet of Things Under The Cushy Foot of Tech Giants The Power of Infinite Funds IoT Toys Bio-robotics Predictive Analytics Machine Learning Artificial Intelligence Cybersecurity Big Data Business Intelligence Augmented Reality Virtual Reality Our Future And much, much more If you want to learn more about the artificial intelligence and internet of things, then scroll up and click "add to cart"!
| Author | : Andrew W. Trask |
| Publisher | : Simon and Schuster |
| Total Pages | : 475 |
| Release | : 2019-01-23 |
| Genre | : Computers |
| ISBN | : 163835720X |
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
| Author | : Gerardus Blokdyk |
| Publisher | : 5starcooks |
| Total Pages | : 310 |
| Release | : 2019-07-03 |
| Genre | : |
| ISBN | : 9780655811039 |
Is AI Machine Learning realistic, or are you setting yourself up for failure? Do you think you know, or do you know you know ? What are the long-term AI Machine Learning goals? What are your results for key measures or indicators of the accomplishment of your AI Machine Learning strategy and action plans, including building and strengthening core competencies? How long will it take to change? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make AI Machine Learning investments work better. This AI Machine Learning All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth AI Machine Learning Self-Assessment. Featuring 943 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which AI Machine Learning improvements can be made. In using the questions you will be better able to: - diagnose AI Machine Learning projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in AI Machine Learning and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the AI Machine Learning Scorecard, you will develop a clear picture of which AI Machine Learning areas need attention. Your purchase includes access details to the AI Machine Learning self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific AI Machine Learning Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
| Author | : Steven Alex |
| Publisher | : |
| Total Pages | : 135 |
| Release | : 2019-11-06 |
| Genre | : |
| ISBN | : 9781706195856 |
★ ★ Buy the Paperback Version of this Book and Get the Kindle Book version for FREE ★ ★ Machine Learning (Update Edition 2019-2020) this Guide is a branch of artificial intelligence, This Machine Learning Series idea is relatively new. A science that researches machines to acquire new knowledge and new skills and to identify existing knowledge. The best way to understand the potential of machine learning is to explore how people and companies are currently taking advantage of it.If you are one of the almost 400 million people with machine learning worldwide, This book offers a method to Techniques! Not every machine learning model uses the same techniques, so training will depend on your approach. Let's consider a few examples: Psychology of learning Machine learning in practice Reinforcement learning Types of machine learning Learning by reinforcement Types of reinforcement The different types of learning This guidebook is going to take some time to explore machine learning, and what it is all about. There are so many different aspects of machine learning and how to make it work for your needs, and all of it is found in this guidebook. Some of the different topics that you will be able to learn about inside include: Neural networks Historical background Why use neural networks? Tasks of neural networks Deep learning Algorithms Starting with python Basic types of data Get access to free software and data sets so you can try out your very own machine learning software. See how advanced machine learning will impact our world in the future! Scroll Up and Click the Buy Now Button!
| Author | : Melanie Mitchell |
| Publisher | : Farrar, Straus and Giroux |
| Total Pages | : 336 |
| Release | : 2019-10-15 |
| Genre | : Computers |
| ISBN | : 0374715238 |
Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.
| Author | : Gerardus Blokdyk |
| Publisher | : 5starcooks |
| Total Pages | : 306 |
| Release | : 2019-07-18 |
| Genre | : |
| ISBN | : 9780655827429 |
Would you develop a AI and machine learning Communication Strategy? Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a AI and machine learning process, are the records needed as inputs to the AI and machine learning process available? Is there a clear AI and machine learning case definition? What are specific AI and machine learning rules to follow? Have you achieved AI and machine learning improvements? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make AI And Machine Learning investments work better. This AI And Machine Learning All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth AI And Machine Learning Self-Assessment. Featuring 906 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which AI And Machine Learning improvements can be made. In using the questions you will be better able to: - diagnose AI And Machine Learning projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in AI And Machine Learning and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the AI And Machine Learning Scorecard, you will develop a clear picture of which AI And Machine Learning areas need attention. Your purchase includes access details to the AI And Machine Learning self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific AI And Machine Learning Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
| Author | : Jon Krohn |
| Publisher | : Addison-Wesley Professional |
| Total Pages | : 725 |
| Release | : 2019-08-05 |
| Genre | : Computers |
| ISBN | : 0135121728 |
"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
| Author | : Oliver Tensor |
| Publisher | : |
| Total Pages | : 368 |
| Release | : 2019-08-21 |
| Genre | : |
| ISBN | : 9781687763105 |
Do you want to learn the progress made in the web marketing space and how you can exploit it for your marketing strategies? Do you want to gain an edge over your business's competitors?If you want to know How Machine Learning and Artificial Intelligence Technology can give your business a major performance boost, then keep reading. The Fourth Industrial Revolution is upon us, led by the Artificial Intelligence technology and setting the humankind for a global social transformation. The powerful applications of AI have already transformed our daily lives. Tools such as virtual personal and home assistants (like Siri in Apple Pods and Alexa in Amazon Echo) have become everyday usage products. Moreover, our digital lives have inundated organizations with astronomical volumes of data with hidden treasures of valuable insights. This information can be uncovered with the use of big data analytics and applied in combination with the Artificial Intelligence technology to increase your business performance efficiency. Learning to incorporate the Artificial Intelligence applications, Machine Learning, and Big Data Analytics in line with your company's domain can only give your business positive results. Machine Learning: The Definive Guide includes 3 books - Machine Learning for Beginners - Artificial Intelligence Business Applications - Artificial Intelligence and Machine Learning for Business Our aim with this book is to provide you a 360 view of the fundamentals and importance of Machine Learning and Artificial Intelligence Technology. You Will Learn: The Fundamentals of Artificial Intelligence and Machine Learning Applications, and Why are They so Important in the World Today. Gain an In-depth Understanding of 12 of the Most Popular Artificial Intelligence Tools in the Market, in an Easy to Understand and Colloquial Language. The Science of Big Data and How Companies are Increasingly Employing Good Analytical Tools to Makes Sense of an Estimated 1.7 MB of Data that will be Generated per Second per Person by 2020. What Different Types of Machine Learning Algorithms are and How They Work to Make Machines Able to Learn and Train themselves with Repeated Use. Even if you are a beginner, you will be armed to make sound personal and professional technological choices. Would You Like to Know More? Download Now to get access to Artificial Intelligence power. Scroll to the top of the page and select BUY NOW button
| Author | : Christoph Molnar |
| Publisher | : Lulu.com |
| Total Pages | : 320 |
| Release | : 2020 |
| Genre | : Artificial intelligence |
| ISBN | : 0244768528 |
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
| Author | : Ian Goodfellow |
| Publisher | : MIT Press |
| Total Pages | : 801 |
| Release | : 2016-11-10 |
| Genre | : Computers |
| ISBN | : 0262337371 |
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.
