Neural Network Tutorials Herong S Tutorial Examples
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Neural Network Tutorials Herong S Tutorial Examples
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Author : Herong Yang
language : en
Publisher: HerongYang.com
Release Date : 2021-03-06
Neural Network Tutorials Herong S Tutorial Examples written by Herong Yang and has been published by HerongYang.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-06 with Computers categories.
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.
Python Tutorials Herong S Tutorial Examples
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Author : Herong Yang
language : en
Publisher: HerongYang.com
Release Date : 2022-04-01
Python Tutorials Herong S Tutorial Examples written by Herong Yang and has been published by HerongYang.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-01 with Computers categories.
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.
Mac Tutorials Herong S Tutorial Examples
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Author : Herong Yang
language : en
Publisher: HerongYang.com
Release Date : 2022-01-01
Mac Tutorials Herong S Tutorial Examples written by Herong Yang and has been published by HerongYang.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Computers categories.
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.
Html Tutorials Herong S Tutorial Examples
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Author : Dr. Herong Yang
language : en
Publisher: Herong Yang
Release Date : 2021-05-01
Html Tutorials Herong S Tutorial Examples written by Dr. Herong Yang and has been published by Herong Yang this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-01 with Computers categories.
This tutorial book is a collection of notes and sample codes written by the author while he was learning HTML himself. Topics include HTML5 and HTML 4.01 standards; HTML document structure; HTML element and attribute syntax; embedding SVG to generate graphics; embedding JavaScript code; adding (CSS Cascading Style Sheets) for display format; displayed and printed versions of HTML documents; responsive design of Web pages; MathML integration in HTML documents. Updated in 2023 (Version v2.30) on MathML tutorials. For latest updates and free sample chapters, visit https://www.herongyang.com/HTML.
Neural Networks For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2020-10-30
Neural Networks For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-30 with categories.
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.
Neural Network Programming With Tensorflow
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Author : Manpreet Singh Ghotra
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-11-10
Neural Network Programming With Tensorflow written by Manpreet Singh Ghotra 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 2017-11-10 with Computers categories.
Neural Networks and their implementation decoded with TensorFlow About This Book Develop a strong background in neural network programming from scratch, using the popular Tensorflow library. Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more. A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation. Who This Book Is For This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you. What You Will Learn Learn Linear Algebra and mathematics behind neural network. Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks. Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points Learn through real world examples like Sentiment Analysis. Train different types of generative models and explore autoencoders. Explore TensorFlow as an example of deep learning implementation. In Detail If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs. Style and Approach This book is designed to give you just the right number of concepts to back up the examples. With real-world use cases and problems solved, this book is a handy guide for you. Each concept is backed by a generic and real-world problem, followed by a variation, making you independent and able to solve any problem with neural networks. All of the content is demystified by a simple and straightforward approach.
Neural Networks For Beginners
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Author : daniel Huston
language : en
Publisher: Lulu.com
Release Date : 2023-05-01
Neural Networks For Beginners written by daniel Huston and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-01 with Computers categories.
"Neural Networks for Beginners" is a beginner-friendly guide to understanding the basics of neural networks, machine learning, and deep learning. Written in simple language, this book provides a comprehensive introduction to the key concepts and techniques used in neural networks. Starting with an overview of the history and importance of neural networks, the book covers the basics of machine learning and deep learning, including their differences and applications. It then delves into the different types of neural networks, their architectures, and how they are trained and optimized. The book also provides real-world examples of successful neural network applications in various fields, such as healthcare, finance, and technology. It explains how neural networks are used in practical applications, such as image recognition, speech recognition, and natural language processing. "Neural Networks for Beginners" is perfect for anyone with no prior knowledge of neural networks who wants to learn about this exciting field. Whether you are a student, researcher, or professional, this book will provide you with the knowledge and skills needed to get started with neural networks. With this book, you'll gain a solid understanding of the basics of neural networks and be prepared to explore and leverage their power. I. Introduction Explanation of neural networks and their applications Neural networks are a type of machine learning algorithm that is modeled after the structure and function of the human brain. They are designed to identify patterns in data and learn from them in order to make predictions or classifications. One of the key applications of neural networks is in image and speech recognition, where the network is trained on large datasets of images or audio files, and can then accurately identify and classify new images or audio recordings. Neural networks can also be used for natural language processing, where they can be trained to understand and respond to written or spoken language. Neural networks are also used in finance for fraud detection and risk assessment, in healthcare for disease diagnosis and treatment planning, in transportation for autonomous vehicles, and in many other fields. One of the main advantages of neural networks is their ability to learn and improve over time, as more data is fed into the network. This makes them ideal for applications where accuracy is critical and where the underlying patterns may be complex
Neural Networks With Python
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Author : Mei Wong
language : en
Publisher: GitforGits
Release Date : 2023-11-02
Neural Networks With Python written by Mei Wong and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-02 with Computers categories.
"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning. In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge. Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise. By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future. Key Learnings Master Python for machine learning, from setup to complex models. Gain flexibility with diverse neural network architectures for various problems. Hands-on experience in building, training, and fine-tuning neural networks. Learn strategic approaches for troubleshooting and optimizing neural models. Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms. Acquire skills in crucial data preprocessing and augmentation techniques. Understand and apply optimization techniques and hyperparameter tuning. Implement an end-to-end machine learning project, from data to deployment. Table of Content Python, TensorFlow, and your First Neural Network Deep Dive into Feedforward Networks Convolutional Networks for Visual Tasks Recurrent Networks for Sequence Data Data Generation with GANs Transformers for Complex Tasks Autoencoders for Data Compression and Generation Capsule Networks
Introduction To Artificial Neural Networks
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Author : Sivanandam S., Paulraj M
language : en
Publisher: Vikas Publishing House
Release Date : 2009-11-01
Introduction To Artificial Neural Networks written by Sivanandam S., Paulraj M and has been published by Vikas Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-11-01 with Computers categories.
This fundamental book on Artificial Neural Networks has its emphasis on clear concepts, ease of understanding and simple examples. Written for undergraduate students, the book presents a large variety of standard neural networks with architecture, algorithms and applications.
Exploring Neural Networks With C
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Author : Ryszard Tadeusiewicz
language : en
Publisher: CRC Press
Release Date : 2014-09-02
Exploring Neural Networks With C written by Ryszard Tadeusiewicz and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-02 with Computers categories.
The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations—making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the important properties of neural networks—while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand. Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks. Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programy&lang=en