Make Your Own Neural Network An In Depth Visual Introduction For Beginners
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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.
The Math Of Neural Networks
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Author : Michael Taylor
language : en
Publisher: Independently Published
Release Date : 2017-10-04
The Math Of Neural Networks 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.
There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network?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. In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network.
Neural Networks For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2021-02-04
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 2021-02-04 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 Networks And Deep Learning
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2018-08-25
Neural Networks And Deep Learning written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-25 with Computers categories.
This book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.
Complex Valued Nonlinear Adaptive Filters
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Author : Danilo P. Mandic
language : en
Publisher: John Wiley & Sons
Release Date : 2009-04-20
Complex Valued Nonlinear Adaptive Filters written by Danilo P. Mandic 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 2009-04-20 with Science categories.
This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). It brings together adaptive filtering algorithms for feedforward (transversal) and feedback architectures and the recent developments in the statistics of complex variable, under the powerful frameworks of CR (Wirtinger) calculus and augmented complex statistics. This offers a number of theoretical performance gains, which is illustrated on both stochastic gradient algorithms, such as the augmented complex least mean square (ACLMS), and those based on Kalman filters. This work is supported by a number of simulations using synthetic and real world data, including the noncircular and intermittent radar and wind signals.
Artificial Intelligence With Python
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27
Artificial Intelligence With Python written by Prateek Joshi 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 2017-01-27 with Computers categories.
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
Bayes Theorem Examples
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Author : Dan Morris
language : en
Publisher: Independently Published
Release Date : 2016-10-02
Bayes Theorem Examples written by Dan Morris and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-02 with Bayesian statistical decision theory categories.
***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples "...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem..." - Doug E. - TOP 100 REVIEWER"...For those who are predominately "Visual Learners", as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact." - Tin F. TOP 50 REVIEWER
Deep Learning Illustrated
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Author : Jon Krohn
language : zh-CN
Publisher:
Release Date : 2021
Deep Learning Illustrated written by Jon Krohn and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Artificial intelligence categories.
An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08
An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.
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.
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.