Download or read book Neural Network Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 2021-03-06 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of notes and sample codes written by the author while he was learning Neural Networks in Machine Learning. Topics include Neural Networks (NN) concepts: nodes, layers, activation functions, learning rates, training sets, etc.; deep playground for classical neural networks; building neural networks with Python; walking through Tariq Rashi's 'Make Your Own Neural Network' source code; using 'TensorFlow' and 'PyTorch' machine learning platforms; understanding CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), GNN (Graph Neural Network). Updated in 2023 (Version v1.22) with minor updates. For latest updates and free sample chapters, visit https://www.herongyang.com/Neural-Network.

Download or read book Python Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 2022-04-01 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Python tutorial book is a collection of notes and sample codes written by the author while he was learning Python language himself. Topics include: installing Python environments on Windows, macOS and Linux computer; Python built-in data types; variables, operations, expressions and statements; user-defined functions; iterators, generators and list comprehensions; modules and packages; sys, os, and pathlib modules; Anaconda Python environment manager; Jupyter Notebooks; NumPy, SciPy libraries. Updated in 2023 (Version v2.14) with minor changes. For latest updates and free sample chapters, visit https://www.herongyang.com/Python.

Download or read book Mac Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 2022-01-01 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of notes and sample codes written by the author while he was learning macOS. Topics include Macintosh OS history; macOS basic functionalities; storage file systems; reviewing resource usage on running processes; installing productivity and programming tools; installing Java and related tools; installing Apache Web server and MySQL database server; using Keychain Access to manage passwords and certificates. Updated in 2023 (Version v3.07) with minor changes. For latest updates and free sample chapters, visit https://www.herongyang.com/Mac.

Download or read book Neural Networks for Beginners written by Russel R Russo and published by . This book was released on 2020-10-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Buy Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence.

Download or read book Introduction to Deep Learning and Neural Networks with PythonTM written by Ahmed Fawzy Gad and published by Academic Press. This book was released on 2020-11-25 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation

Download or read book Convolutional Neural Networks In Python written by Frank Millstein and published by Frank Millstein. This book was released on 2020-07-06 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book NOW and learn more about Convolutional Neural Networks in Python!

Download or read book Introduction to Deep Learning and Neural Networks with PythonT written by Ahmed Fawzy Gad and published by Academic Press. This book was released on 2020-12-10 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonT examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonT functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonT Features math and code examples (via companion website) with helpful instructions for easy implementation

Download or read book Neural Networks and Deep Learning written by Pat Nakamoto and published by Createspace Independent Publishing Platform. This book was released on 2018-06-30 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: What's Inside? This includes 3 manuscripts: Book 1: Neural Networks & Deep Learning: Deep Learning explained to your granny - A visual introduction for beginners who want to make their own Deep Learning Neural Network... What you will gain from this book: * A deep understanding of how Deep Learning works * A basics comprehension on how to build a Deep Neural Network from scratch Who this book is for: * Beginners who want to approach the topic, but are too afraid of complex math to start! * Two main Types of Machine Learning Algorithms * A practical example of Unsupervised Learning * What are Neural Networks? * McCulloch-Pitts's Neuron * Types of activation function * Types of network architectures * Learning processes * Advantages and disadvantages * Let us give a memory to our Neural Network * The example of book writing Software * Deep learning: the ability of learning to learn * How does Deep Learning work? * Main architectures and algorithms * Main types of DNN * Available Frameworks and libraries * Convolutional Neural Networks * Tunnel Vision * Convolution * The right Architecture for a Neural Network * Test your Neural Network * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks Book2: Deep Learning: Deep Learning explained to your granny - A guide for Beginners... What's Inside? * A general overview of Deep Learning * What are the limits of Deep Learning? * Deep Learning: the basics * Layers, Learning paradigms, Training, Validation * Main architectures and algorithms * Convolutional Neural Networks * Models for Deep Learning * Probabilistic graphic models * Restricted Boltzmann Machines * Deep Belief Networks * Available Frameworks and libraries * TensorFlow Book 3: Big Data: The revolution that is transforming our work, market and world... "Within 2 days we produce the same amount of data generated by at the beginning of the civilization until 2003," said Eric Schmidt in 2010. According to IBM, by 2020 the world will have generated a mass of data on the order of 40 zettabyte (1021Byte). Just think, for example, of digital content such as photos, videos, blogs, posts, and everything that revolves around social networks; only Facebook marks 30 billion pieces of content each month shared by its users. The explosion of social networks, combined with the emergence of smartphones, justifies the fact that one of the recurring terms of recent years in the field of innovation, marketing and IT is "Big Data." The term Big Data indicates data produced in massive quantities, with remarkable rapidity and in the most diverse formats, which require technologies and resources that go far beyond conventional data management and storage systems. In order to obtain from the use of this data the maximum results in the shortest possible time or even in real time, specific tools with high computing capabilities are necessary. But what does the Big Data phenomenon mean? Is the proliferation of data simply the sign of an increasingly invasive world? Or is there something more to it? Pat Nakamoto will guide you through the discovery of the world of Big data, which, according to experts, in the near future could become the new gold or oil, in what is a real Data Driven economy.

Download or read book Beginning Deep Learning with TensorFlow written by Liangqu Long and published by Apress. This book was released on 2022-03-07 with total page 713 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You’ll start with an introduction to AI, where you’ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you’ll jump into simple classification programs for hand-writing analysis. Once you’ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you’ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! What You'll Learn Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications Who This Book Is For Students, programmers, and researchers with no experience in deep learning who want to build up their basic skillsets. Experienced machine learning programmers and engineers might also find value in updating their skills.

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Android Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 2021-05-01 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of notes and sample codes written by the author while he was learning Android system. Topics include Installing of Android SDK on Windows, Creating and running Android emulators, Developing First Android Application - HelloAndroid, Creating Android Project with 'android' Command, Building, Installing and Running the Debug Binary Package, Inspecting Android Application Package (APK) Files, Using Android Debug Bridge (adb) Tool, Copying files from and to Android device, Understanding Android File Systems, Using Android Java class libraries, Using 'adb logcat' Command for Debugging. Updated in 2023 (Version v3.05) with ADB tutorials. For latest updates and free sample chapters, visit https://www.herongyang.com/Android.

Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Download or read book MySQL Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 1999-01-01 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: This MySQL tutorial book is a collection of notes and sample codes written by the author while he was learning MySQL himself, an ideal tutorial guide for beginners. Topics include introduction of Structured Query Language (SQL); installation of MySQL server on Windows, Linux, and macOS; using MySQL client program; accessing MySQL server from PHP, Java and Perl programs; SQL data types, literals, operations, expressions, and functions; Statements of Data Definition Language (DDL), Data Manipulation Language (DML), and Query Language; creating and using indexes; using window functions; stored procedures; transaction management; locks and deadlocks; InnoDB and other storage engines. Updated in 2023 (Version v4.46) with minor changes. For latest updates and free sample chapters, visit https://www.herongyang.com/MySQL.

Download or read book XSL FO Tutorials Herong s Tutorial Examples written by Herong Yang and published by HerongYang.com. This book was released on 2006-01-01 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of tutorial notes and sample codes written by the author while he was learning XSL-FO (Extensible Stylesheet Language - Formatting Objects) himself. Topics include: Introduction to XSL, XSL-FO, and Apache FOP; XSL concepts: Area Model, Inline Stacking and Block Stacking; Page layouts: simple-page-master and page-sequence-master; Page regions: region-body, region-before, region-after, region-start and region-end; Formatting objects: Block-Level and Inline-Level Objects; Adding Graphics from Files and SVG Elements; Managing DPI Resolution; Building Tables of Rows and Columns; Managing Lists of Items and Floating Blocks; Adding External and Internal Hyperlinks; Building Table of Contents; Adding Page Headers and Footers; Managing Fonts: Generic Fonts; Adobe Base-14 Fonts; Embedding Fonts; HTML with SVG and MathML to PDF conversion. Updated in 2024 (Version v2.25) with minor updates. For latest updates and free sample chapters, visit https://www.herongyang.com/XSL-FO.

Download or read book Demystifying the Brain written by V. Srinivasa Chakravarthy and published by Springer. This book was released on 2018-12-07 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an emerging new vision of the brain, which is essentially expressed in computational terms, for non-experts. As such, it presents the fundamental concepts of neuroscience in simple language, without overwhelming non-biologists with excessive biological jargon. In addition, the book presents a novel computational perspective on the brain for biologists, without resorting to complex mathematical equations. It addresses a comprehensive range of topics, starting with the history of neuroscience, the function of the individual neuron, the various kinds of neural network models that can explain diverse neural phenomena, sensory-motor function, language, emotions, and concluding with the latest theories on consciousness. The book offers readers a panoramic introduction to the “new brain” and a valuable resource for interdisciplinary researchers looking to gatecrash the world of neuroscience.

Download or read book Artificial Neural Networks in Real life Applications written by Juan Ramon Rabunal and published by IGI Global. This book was released on 2006-01-01 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book offers an outlook of the most recent works at the field of the Artificial Neural Networks (ANN), including theoretical developments and applications of systems using intelligent characteristics for adaptability"--Provided by publisher.

Download or read book How to Do Nothing written by Jenny Odell and published by Melville House. This book was released on 2020-12-29 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: ** A New York Times Bestseller ** NAMED ONE OF THE BEST BOOKS OF THE YEAR BY: Time • The New Yorker • NPR • GQ • Elle • Vulture • Fortune • Boing Boing • The Irish Times • The New York Public Library • The Brooklyn Public Library "A complex, smart and ambitious book that at first reads like a self-help manual, then blossoms into a wide-ranging political manifesto."—Jonah Engel Bromwich, The New York Times Book Review One of President Barack Obama's "Favorite Books of 2019" Porchlight's Personal Development & Human Behavior Book of the Year In a world where addictive technology is designed to buy and sell our attention, and our value is determined by our 24/7 data productivity, it can seem impossible to escape. But in this inspiring field guide to dropping out of the attention economy, artist and critic Jenny Odell shows us how we can still win back our lives. Odell sees our attention as the most precious—and overdrawn—resource we have. And we must actively and continuously choose how we use it. We might not spend it on things that capitalism has deemed important … but once we can start paying a new kind of attention, she writes, we can undertake bolder forms of political action, reimagine humankind’s role in the environment, and arrive at more meaningful understandings of happiness and progress. Far from the simple anti-technology screed, or the back-to-nature meditation we read so often, How to do Nothing is an action plan for thinking outside of capitalist narratives of efficiency and techno-determinism. Provocative, timely, and utterly persuasive, this book will change how you see your place in our world.