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Deep Learning For Dummies

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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 For Dummies

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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2019-05-14

Deep 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 2019-05-14 with Computers categories.

Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Machine Learning For Dummies

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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2021-02-09

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learning While machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more. Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Deep Learning For Beginners

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Author : Dr. Pablo Rivas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-09-18

Deep Learning For Beginners written by Dr. Pablo Rivas 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 2020-09-18 with Computers categories.

Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlow Key FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook Description With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning. This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and you already have the basic mathematical and programming knowledge required to get started. The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples, and learn how to build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book. By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks. What you will learnImplement recurrent neural networks (RNNs) and long short-term memory (LSTM) for image classification and natural language processing tasksExplore the role of convolutional neural networks (CNNs) in computer vision and signal processingDiscover the ethical implications of deep learning modelingUnderstand the mathematical terminology associated with deep learningCode a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent spaceImplement visualization techniques to compare AEs and VAEsWho this book is for This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.

Deep Learning For Beginners

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Author : François Duval
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-12-24

Deep Learning For Beginners written by François Duval 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 2017-12-24 with categories.

***** Buy now (Will soon return to $35.99)***** ***** #1 Kindle Store Bestseller in Mathematical Analysis (Throughout 2017) ***** Free Kindle eBook for customers who purchase the print book Are you thinking of learning more about Deep Learning? If you are looking for a book to help you understand how the deep learning works by using Python and Tensorflow, then this is a good book for you. Several Visual Illustrations and Examples Equations are great for really understanding every last detail of an algorithm. But to get a basic idea of how things work, this book contains several graphs which detail each neural networks/deep learning algorithms. It is contains also several graphs for the practical examples. This Is a Practical Guide Book This book will help you explore exactly what deep learning is and will also teach you about why it is so revolutionary and fascinating. The chapters will introduce the reader to the concepts, techniques, and applications of deep learning algorithms with the practical case studies and walk-through examples on which to practice. This book takes a different approach that is based on providing simple examples of how deep learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. Python and TensorFlow Codes for the Examples Shown In the Book You will build your Deep Learning Model by using Python and Tensorflow There are many ways to build a deep learning model. However, it can also be overwhelming when you start, because there are so many tools to choose. In this book, we choose only these two tools: Tensorflow and Python. Target Users The book designed for a variety of target audiences. The most suitable users would include: Newbies in computer science techniques and deep learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on neural networks and deep learning What's inside this book? Overview in Deep Learning Quick Example to start Popular Open Source Library Pre-requisite for Deep Learning Deep Learning Presentation Deep Neural Networks Applications with Tensorflow and Python Autoencoders Algorithms Deep Learning for Computer Games Anomaly Detection Glossary of Some Useful Terms in Deep Learning Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to smash deep learning with Python, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you will be OK. If not, online programming courses cover more than what it is required. You can do one in a week, for free. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a deep learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in deep learning and further learning will be required beyond this book to master all aspects of deep learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at customer_service@datasciences-book.com.

Deep Learning For Beginners

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Author : François Duval
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-01-13

***** Buy now (Will soon return to $38.99 + Special Offer Below) ***** ***** #1 Kindle Store Bestseller in Computer Modelling ***** Free Kindle eBook for customers who purchase the print book from Amazon Are you thinking of learning more about Deep Learning? If you are looking for a book to help you understand concepts and algorithms of deep learning, then this is a good book for you. Several Visual Illustrations and Examples Equations are great for really understanding every last detail of an algorithm. But to get a basic idea of how things work, this book contains several graphs which detail each neural networks/deep learning algorithms. It is contains also several graphs for the practical examples. This Is a Practical Guide Book This book will help you explore exactly what deep learning is and will also teach you about why it is so revolutionary and fascinating. The chapters will introduce the reader to the concepts, techniques, and applications of deep learning algorithms with the practical case studies and walk-through examples on which to practice. This book takes a different approach that is based on providing simple examples of how deep learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. Target Users The book designed for a variety of target audiences. The most suitable users would include: Newbies in computer science techniques and deep learning Professionals in data science and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on neural networks and deep learning What's inside this book? Pre-requisite for Deep Learning Introduction to Artificial Neural Networks The Basics of Artificial Neural Networks Deep Learning Evolution and Recurring Methods Relationship between machine learning and deep learning Multilayer Perceptron (MLP) Convolutional Neural Networks (CNN) Other Deep Learning Algorithms Deep Learning Applications Glossary of Some Useful Terms in Deep Learning Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: If you want to learn more about deep learning, this book is for you. Little math knowledge is required. If you already have a basic notion in statistic and data science, you'll be OK. No coding experience is required. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a deep learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in deep learning and further learning will be required beyond this book to master all aspects of deep learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. will also be happy to help you if you send us an email at customer_service@datasciences-book.com.

Artificial Intelligence For Dummies

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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2018-03-16

Artificial Intelligence 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 2018-03-16 with Computers categories.

Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it’s being used today. Inside, you’ll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more accessible than ever!

Deep Learning For Beginners

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Author : Thomas Laville
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-10-30

Deep Learning For Beginners written by Thomas Laville 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 2017-10-30 with categories.

Thinking of learning more in Deep Learning? Then you have landed in the right place. The overall aim of this book in Deep Learning is to explore and examine key concepts, methods and techniques used in the Deep Learning. Then you have landed in the right place. The overall aim of this book in Deep Learning is to explore and examine key concepts, methods and techniques used in the Deep Learning. This book will help you explore exactly what deep learning is and will also teach you about why it is so revolutionary and fascinating. The 11 chapters introduce the reader the concepts, techniques, application of Dep Leaning Algorithm with the practical case studies and walk-through examples to practice. By the time you are done reading this book, you will have a complete understanding as to what deep learning is and why it is such an incredible advancement in technology. Chapters in this book Introduction to Deep Learning Fundamental Concepts of Deep Learning Artificial Neural Networks Deep Neural Networks Deep Learning Applications Glossary of important terms And more Book Objectives To have an appreciation for Deep Learning and an understanding of their fundamental principles. To have an elementary adeptness in a Deep Learning Concepts and terms which includes an ability to understand the algorithms. To have an elementary understanding of (some of the) more advanced topics of Deep Learning such as Neural Networks, Deep Neural Networks. Target Users The book designed for a variety of target audiences. The most suitable users would include: 1. Newbies in Computer Science Techniques and Artificial Intelligence 2. Professionals in Data scientist and Social Sciences 3. Professors or lecturers or tutors to be in position to find better ways to explain the content to their students with simples and easiest way 4. The students and Academicians, especially those that are focusing on Deep Learning as their professionsScroll to the top and buy now to get started.

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!

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.

Deep Learning For Dummies

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