Make Your Own Neural Network Book PDF, EPUB Download & Read Online Free

Make Your Own Neural Network
Author: Tariq Rashid
Publisher: Createspace Independent Publishing Platform
ISBN: 1530826608
Pages: 222
Year: 2016-03-31
View: 647
Read: 449
A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.
Make Your Own Neural Network: an In-Depth Visual Introduction for Beginners
Author: Michael Taylor
ISBN: 1549869132
Pages: 248
Year: 2017-10-04
View: 1259
Read: 894
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.
Build Your Own Neural Network Today!
Author: N. D. Lewis
Publisher: Createspace Independent Publishing Platform
ISBN: 1519101236
Pages: 224
Year: 2015-11-18
View: 155
Read: 1071
BUILD YOUR OWN NEURAL NETWORK TODAY! With an EASY to follow process showing you how to build them FASTER than you imagined possible using R About This Book This rich, fascinating, accessible hands on guide, puts neural networks firmly into the hands of the practitioner. It reveals how they work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful, free R predictive analytics package. Everything you need to get started is contained within this book. It is your detailed, practical, tactical hands on guide. To accelerate your success, it contains exercises with fully worked solutions also provided. Once you have mastered the process, it will be easy for you to translate your knowledge into other powerful applications. A book for everyone interested in machine learning, predictive analytics, neural networks and decision science. Here is what it can do for you: SAVE TIME: Imagine having at your fingertips easy access to the very best neural network models without getting bogged down in mathematical details. In this book, you'll learn fast effective ways to build powerful neural network models easily using R. LEARN EASILY: Build Your Own Neural Network TODAY! Contains an easy to follow process showing you how to build the most successful neural networks used for learning from data; use this guide and build them easily and quickly. BOOST PRODUCTIVITY: Bestselling author and data scientist Dr. N.D. Lewis will show you how to build neural network models in less time than you ever imagined possible! Even if you're a busy professional, a student or hobbyist with little time, you will rapidly enhance your knowledge. EFFORTLESS SUCCESS: By spending as little as 10 minutes a day working through the dozens of real world examples, illustrations, practitioner tips and notes, you'll be able to make giant leaps forward in your knowledge, broaden your skill-set and generate new ideas for your own personal use. ELIMINATE ANXIETY: Forget trying to master every single mathematical detail, instead your goal is to simply to follow the process using real data that only takes about 5 to 15 minutes to complete. Within this process is a series of actions by which the neural network model is explained and constructed. All you have to do is follow the process. It is your checklist for use and reuse. 1 For people interested in statistics, machine learning, data analysis, data mining, and future hands-on practitioners seeking a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. Here are some of the neural network models you will build: Multi layer Perceptrons Probabilistic Neural Networks Generalized Regression Neural Networks Recurrent Neural Networks Buy the book today. Your next big breakthrough using neural networks is only a page away!
Tensorflow in 1 Day: Make Your Own Neural Network
Author: Krishna Rungta
Publisher: Independently Published
ISBN: 1720092257
Pages: 366
Year: 2018-09-05
View: 978
Read: 572
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
Neural Network Programming With Python
Author: Max Sharp
ISBN: 1539381951
Year: 2016-10-18
View: 276
Read: 994
This book is a guide on how to implement a neural network in the Python programming language. It begins by giving you a brief overview of neural networks so as to know what they are, where they are used, and how they are implemented. The next step is an exploration of the backpropagation algorithm. This is the algorithm behind the functionality of neural networks, and it involves a forward and backward pass. Numby is a Python library which can be used for the purpose of implementation of a neural network. This library is discussed in this book, and you are guided on how to use it for that purpose. The functionality of neural networks has to be improved. The various ways to improve how a neural network works is also explored. You are then guided on how to implement neural networks with Neupy, another Python library. The following topics are discussed in this book: - A Brief Overview of Neural Networks - Backpropagation Algorithm - Neural Networks with Numpy - Improving a Neural Network in Python - Neupy - Models in Neural Networks
An Introduction to Neural Networks
Author: Kevin Gurney
Publisher: CRC Press
ISBN: 1482286998
Pages: 234
Year: 2014-04-21
View: 954
Read: 964
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Neural Networks for Applied Sciences and Engineering
Author: Sandhya Samarasinghe
Publisher: CRC Press
ISBN: 1420013068
Pages: 570
Year: 2016-04-19
View: 177
Read: 523
In response to the exponentially increasing need to analyze vast amounts of data, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition provides scientists with a simple but systematic introduction to neural networks. Beginning with an introductory discussion on the role of neural networks in scientific data analysis, this book provides a solid foundation of basic neural network concepts. It contains an overview of neural network architectures for practical data analysis followed by extensive step-by-step coverage on linear networks, as well as, multi-layer perceptron for nonlinear prediction and classification explaining all stages of processing and model development illustrated through practical examples and case studies. Later chapters present an extensive coverage on Self Organizing Maps for nonlinear data clustering, recurrent networks for linear nonlinear time series forecasting, and other network types suitable for scientific data analysis. With an easy to understand format using extensive graphical illustrations and multidisciplinary scientific context, this book fills the gap in the market for neural networks for multi-dimensional scientific data, and relates neural networks to statistics. Features § Explains neural networks in a multi-disciplinary context § Uses extensive graphical illustrations to explain complex mathematical concepts for quick and easy understanding ? Examines in-depth neural networks for linear and nonlinear prediction, classification, clustering and forecasting § Illustrates all stages of model development and interpretation of results, including data preprocessing, data dimensionality reduction, input selection, model development and validation, model uncertainty assessment, sensitivity analyses on inputs, errors and model parameters Sandhya Samarasinghe obtained her MSc in Mechanical Engineering from Lumumba University in Russia and an MS and PhD in Engineering from Virginia Tech, USA. Her neural networks research focuses on theoretical understanding and advancements as well as practical implementations.
Deep Learning for Natural Language Processing
Author: Palash Goyal, Sumit Pandey, Karan Jain
Publisher: Apress
ISBN: 1484236858
Pages: 277
Year: 2018-06-26
View: 987
Read: 306
Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You’ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.
Neural Networks for Beginners
Author: Bob Story
Publisher: Createspace Independent Publishing Platform
ISBN: 1548960292
Pages: 56
Year: 2017-07-15
View: 1024
Read: 850
Discover How to Build Your Own Neural Network From Scratch...Even if You've Got Zero Math or Coding Skills! What seemed like a lame and unbelievable sci-fi movie a few decades ago is now a reality. Machines can finally think. Maybe not quite as complex as the human brain, but more than enough to make everyone's life a lot easier. Artificial neural networks, based on the neurons found in the human brain give machines a 'brain'. Patterned just like biological neurons, these software or hardware are a variety of the deep learning technology. With their help you can make your computer learn by feeding it data, which will then be generated as the output you desire. It is they to thank for the nanoseconds in which computers operate. It may be science, but it is not actually rocket science. Everyone can learn how to take advantage of the progressed technology of today, get inside the 'brain' of the computers, and train them to perform the desired operations. They have been used in many different industries, and you can rest assured that you will find the perfect purpose for your own neural network. The best part about this book is that it doesn't require a college degree. Your high school math skills are quite enough for you to get a good grasp of the basics and learn how to build an artificial neural network. From non-mathematical explanations to teaching you the basic math behind the ANNs and training you how to actually program one, this book is the most helpful guide you will ever find. Carefully designed for you, the beginner, this guide will help you become a proud owner of a neural network in no time. Here's a Sneak Peak to What You'll Discover Inside this Book: The 6 unique benefits of neural networks The difference between biological and artificial neural networks And inside look into ANN (Artificial Neural Networks) The industries ANN is used in How to teach neural networks to perform specific commands The different types of learning modalities (e.g. Hebbian Learning, unsupervised learning, supervised learning etc.) The architecture of ANN Basic math behind artificial neurons Simple networks for pattern classification The Hebb Rule How to build a simple neural network code The backpropogation algorithm and how to program it And much, much more! There's a lot more inside this book we'll cover, so be prepared. I've made to lucidly explain everything I cover so that there's zero confusion! Download this book today and discover all the intricate details of building your very own Neural Network
Artificial Intelligence for Humans, Volume 3
Author: Jeff Heaton
Publisher: Createspace Independent Publishing Platform
ISBN: 1505714346
Pages: 374
Year: 2015-10-28
View: 1204
Read: 990
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
Python Artificial Intelligence Projects for Beginners
Author: Joshua Eckroth
Publisher: Packt Publishing Ltd
ISBN: 1789538246
Pages: 162
Year: 2018-07-31
View: 1034
Read: 1096
Build smart applications by implementing real-world artificial intelligence projects Key Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book Description Artificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence. This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library. By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progress What you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is for Python Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with code
Fundamentals of Deep Learning
Author: Nikhil Buduma, Nicholas Locascio
Publisher: "O'Reilly Media, Inc."
ISBN: 1491925566
Pages: 298
Year: 2017-05-25
View: 1021
Read: 1153
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning
Convolutional Neural Networks in Python
Author: Anthony Williams
Publisher: Createspace Independent Publishing Platform
ISBN: 1973908786
Pages: 106
Year: 2017-07-25
View: 1272
Read: 1224
Convolutional Neural Networks in Python (2nd Edition) Deep learning has been a great part of various scientific fields and since this is my third book regarding this topic, you already know the great significance of deep learning in comparison to traditional methods. At this point, you are also familiar with types of neural networks and their wide range of applications including image and speech recognition, natural language processing, video game development and other. On the other hand, this book is all about convolutional neural networks and how to use these neural networks in various tasks of automatic image and speech recognition in Python. You will also get a better insight into the architecture of convolutional layers as we are going deeper into this subject. Deep learning is pretty complex subject, but since you already have a fundamental knowledge of this topic, getting to know convolutional neural networks better is next logical step. What you will learn in Convolutional Neural Networks in Python: Architecture of convolutional neural networks Solving computer vision tasks using convolutional neural networks Python and computer vision Automatic image and speech recognition Theano and TenroeFlow image recognition How to use MNIST vision dataset What are commonly used convolutional filters Get this book today and learn more about Convolutional Neural Networks in Python!! PS: Get the Paperback and get this Ebook for FREE!!
Neural Network Programming with Java
Author: Fabio M. Soares, Alan M. F. Souza
Publisher: Packt Publishing Ltd
ISBN: 1787122972
Pages: 270
Year: 2017-03-14
View: 785
Read: 1261
Create and unleash the power of neural networks by implementing professional Java code About This Book Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition Explore the Java multi-platform feature to run your personal neural networks everywhere This step-by-step guide will help you solve real-world problems and links neural network theory to their application Who This Book Is For This book is for Java developers who want to know how to develop smarter applications using the power of neural networks. Those who deal with a lot of complex data and want to use it efficiently in their day-to-day apps will find this book quite useful. Some basic experience with statistical computations is expected. What You Will Learn Develop an understanding of neural networks and how they can be fitted Explore the learning process of neural networks Build neural network applications with Java using hands-on examples Discover the power of neural network's unsupervised learning process to extract the intrinsic knowledge hidden behind the data Apply the code generated in practical examples, including weather forecasting and pattern recognition Understand how to make the best choice of learning parameters to ensure you have a more effective application Select and split data sets into training, test, and validation, and explore validation strategies In Detail Want to discover the current state-of-art in the field of neural networks that will let you understand and design new strategies to apply to more complex problems? This book takes you on a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java, giving you everything you need to stand out. You will first learn the basics of neural networks and their process of learning. We then focus on what Perceptrons are and their features. Next, you will implement self-organizing maps using practical examples. Further on, you will learn about some of the applications that are presented in this book such as weather forecasting, disease diagnosis, customer profiling, generalization, extreme machine learning, and characters recognition (OCR). Finally, you will learn methods to optimize and adapt neural networks in real time. All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience. Style and approach This book takes you on a steady learning curve, teaching you the important concepts while being rich in examples. You'll be able to relate to the examples in the book while implementing neural networks in your day-to-day applications.
Neural Network Learning
Author: Martin Anthony, Peter L. Bartlett
Publisher: Cambridge University Press
ISBN: 052111862X
Pages: 389
Year: 2009-08-20
View: 361
Read: 1029
This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. The authors also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is essentially self-contained, since it introduces the necessary background material on probability, statistics, combinatorics and computational complexity; and it is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

Recently Visited