Make Your Own Neural Network Pdfe4b88be8bdbd

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Make Your Own Neural Network

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Author : Tariq Rashid
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016

Make Your Own Neural Network written by Tariq Rashid 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 2016 with Application software categories.

This book is for anyone who wants to understand what neural network[s] are. It's for anyone who wants to make and use their own. And it's for anyone who wants to appreciate the fairly easy but exciting mathematical ideas that are at the core of how they work. This guide is not aimed at experts in mathematics or computer science. You won't need any special knowledge or mathematical ability beyond school maths [sic] ... Teachers can use this guide as a particularly gentle explanation of neural networks and their implementation to enthuse and excite students making their very own learning artificial intelligence with only a few lines of programming language code. The code has been tested to work with a Raspberry Pi, a small inexpensive computer very popular in schools and with young students"--(page 6, Introduction)

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.

Make Your Own Neural Network An In Depth Visual Introduction For Beginners

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Author : Michael Taylor
language : en
Publisher: Independently Published
Release Date : 2017-10-04

Make Your Own Neural Network An In Depth Visual Introduction For Beginners written by Michael Taylor and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-04 with Computers categories.

A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow. What you will gain from this book: * A deep understanding of how a Neural Network works. * How to build a Neural Network from scratch using Python. Who this book is for: * Beginners who want to fully understand how networks work, and learn to build two step-by-step examples in Python. * Programmers who need an easy to read, but solid refresher, on the math of neural networks. What's Inside - 'Make Your Own Neural Network: An Indepth Visual Introduction For Beginners' What Is a Neural Network? Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? we gently explore these topics so that we can be prepared to dive deep further on. To start, we'll begin with a high-level overview of machine learning and then drill down into the specifics of a neural network. The Math of Neural Networks On a high level, a network learns just like we do, through trial and error. This is true regardless if the network is supervised, unsupervised, or semi-supervised. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. * Forward Propagation * Calculating The Total Error * Calculating The Gradients * Updating The Weights Make Your Own Artificial Neural Network: Hands on Example You will learn to build a simple neural network using all the concepts and functions we learned in the previous few chapters. Our example will be basic but hopefully very intuitive. Many examples available online are either hopelessly abstract or make use of the same data sets, which can be repetitive. Our goal is to be crystal clear and engaging, but with a touch of fun and uniqueness. This section contains the following eight chapters. Building Neural Networks in Python There are many ways to build a neural network and lots of tools to get the job done. This is fantastic, but it can also be overwhelming when you start, because there are so many tools to choose from. We are going to take a look at what tools are needed and help you nail down the essentials. To build a neural network Tensorflow and Neural Networks There is no single way to build a feedforward neural network with Python, and that is especially true if you throw Tensorflow into the mix. However, there is a general framework that exists that can be divided into five steps and grouped into two parts. We are going to briefly explore these five steps so that we are prepared to use them to build a network later on. Ready? Let's begin. Neural Network: Distinguish Handwriting We are going to dig deep with Tensorflow and build a neural network that can distinguish between handwritten numbers. We'll use the same 5 steps we covered in the high-level overview, and we are going to take time exploring each line of code. Neural Network: Classify Images 10 minutes. That's all it takes to build an image classifier thanks to Google! We will provide a high-level overview of how to classify images using a convolutional neural network (CNN) and Google's Inception V3 model. Once finished, you will be able to tweak this code to classify any type of image sets! Cats, bats, super heroes - the sky's the limit.

Tensorflow In 1 Day Make Your Own Neural Network

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Author : Krishna Rungta
language : en
Publisher: Independently Published
Release Date : 2018-09-05

Tensorflow In 1 Day Make Your Own Neural Network written by Krishna Rungta and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-05 with Computers categories.

Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices. Here is what is covered in the book

Build A Computer From Scratch

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Author : Jeff Heaton
language : en
Publisher: Heaton Research, Inc.
Release Date : 2006-06

Build A Computer From Scratch written by Jeff Heaton and has been published by Heaton Research, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06 with Computers categories.

Building a computer system lets users get exactly the computer system that they need. This book takes them through all of the steps to create a powerful computer system. Includes 120+ photographs to guide readers through the process. (Computer Books)

Army Of None Autonomous Weapons And The Future Of War

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Author : Paul Scharre
language : en
Publisher: W. W. Norton & Company
Release Date : 2018-04-24

Army Of None Autonomous Weapons And The Future Of War written by Paul Scharre and has been published by W. W. Norton & Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-24 with History categories.

"The book I had been waiting for. I can't recommend it highly enough." —Bill Gates The era of autonomous weapons has arrived. Today around the globe, at least thirty nations have weapons that can search for and destroy enemy targets all on their own. Paul Scharre, a leading expert in next-generation warfare, describes these and other high tech weapons systems—from Israel’s Harpy drone to the American submarine-hunting robot ship Sea Hunter—and examines the legal and ethical issues surrounding their use. “A smart primer to what’s to come in warfare” (Bruce Schneier), Army of None engages military history, global policy, and cutting-edge science to explore the implications of giving weapons the freedom to make life and death decisions. A former soldier himself, Scharre argues that we must embrace technology where it can make war more precise and humane, but when the choice is life or death, there is no replacement for the human heart.

Learn Tensorflow In 24 Hours

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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-10-31

Learn Tensorflow In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.

Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices. Here is what is covered in the book – Table Of Content Chapter 1: What is Deep learning? Chapter 2: Machine Learning vs Deep Learning Chapter 3: What is TensorFlow? Chapter 4: Comparison of Deep Learning Libraries Chapter 5: How to Download and Install TensorFlow Windows and Mac Chapter 6: Jupyter Notebook Tutorial Chapter 7: Tensorflow on AWS Chapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators Chapter 9: Tensorboard: Graph Visualization with Example Chapter 10: NumPy Chapter 11: Pandas Chapter 12: Scikit-Learn Chapter 13: Linear Regression Chapter 14: Linear Regression Case Study Chapter 15: Linear Classifier in TensorFlow Chapter 16: Kernel Methods Chapter 17: TensorFlow ANN (Artificial Neural Network) Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image Classification Chapter 19: Autoencoder with TensorFlow Chapter 20: RNN(Recurrent Neural Network) TensorFlow

Artificial Intelligence In China

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Author : Qilian Liang
language : en
Publisher: Springer Nature
Release Date : 2020-01-31

Artificial Intelligence In China written by Qilian Liang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-31 with Technology & Engineering categories.

This book brings together papers presented at the International Conference on Artificial Intelligence in China (ChinaAI) 2019, which provided a venue for disseminating the latest advances and discussing the interactions and links between the various subfields of AI. Addressing topics that cover virtually all aspects of AI and the latest developments in China, the book is chiefly intended for undergraduate and graduate students in Electrical Engineering, Computer Science, and Mathematics, for researchers and engineers from academia and industry, and for government employees (e.g. at the NSF, DOD, and DOE).

Recurrent Neural Networks With Python Quick Start Guide

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Author : Simeon Kostadinov
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-30

Recurrent Neural Networks With Python Quick Start Guide written by Simeon Kostadinov 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 2018-11-30 with Computers categories.

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework. Key FeaturesTrain and deploy Recurrent Neural Networks using the popular TensorFlow libraryApply long short-term memory unitsExpand your skills in complex neural network and deep learning topicsBook Description Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling. Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood. After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field. What you will learnUse TensorFlow to build RNN modelsUse the correct RNN architecture for a particular machine learning taskCollect and clear the training data for your modelsUse the correct Python libraries for any task during the building phase of your modelOptimize your model for higher accuracyIdentify the differences between multiple models and how you can substitute themLearn the core deep learning fundamentals applicable to any machine learning modelWho this book is for This book is for Machine Learning engineers and data scientists who want to learn about Recurrent Neural Network models with practical use-cases. Exposure to Python programming is required. Previous experience with TensorFlow will be helpful, but not mandatory.

Guide To Convolutional Neural Networks

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Author : Hamed Habibi Aghdam
language : en
Publisher: Springer
Release Date : 2017-05-17

Guide To Convolutional Neural Networks written by Hamed Habibi Aghdam and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-17 with Computers categories.

This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.