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Author : Justine C. Baker
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.F/5 ( download)
Download or read book A Neural Network Guide to Teaching written by Justine C. Baker and published by . This book was released on 1998 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discusses biological and artificial neural networks, neural network learning and instructional practice.
Author : Ahmed Fawzy Gad
Publisher : Academic Press
ISBN 13 : 0323909345
Total Pages : 302 pages
Book Rating : 4.3/5 (239 download)
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
Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319944630
Total Pages : 497 pages
Book Rating : 4.3/5 (199 download)
Download or read book Neural Networks and Deep Learning written by Charu C. Aggarwal and published by Springer. This book was released on 2018-08-25 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Tariq Rashid
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781530826605
Total Pages : 0 pages
Book Rating : 4.8/5 (266 download)
Download or read book Make Your Own Neural Network written by Tariq Rashid and published by Createspace Independent Publishing Platform. This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
Author : Rudolph Russell
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781718898424
Total Pages : 80 pages
Book Rating : 4.8/5 (984 download)
Download or read book Neural Networks written by Rudolph Russell and published by Createspace Independent Publishing Platform. This book was released on 2018-05-08 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: NEURAL NETWORKS Buy the Paperback version of this book, and get the Kindle eBook version included for FREE! Do You Want to Become An Expert Of Neural Networks?? Start Getting this Book and Follow My Step by Step Explanations! Click Add To Cart Now! This book on neural networks will provide you with an excellent overview of the domain of deep learning neural networks. You will gain an understanding of the conception of neural networks and how biological and artificial neural networks differ from each other. You'll learn about artificial neural networks and understand how neural networks function in general. Finally, you'll learn how to teach your networks. To understand this book, you'll need to understand some preliminary mathematical concepts. This book contains illustrations and step-by-step explanations with bullet points and exercises for easy and enjoyable learning Benefits of reading this book that you're not going to find anywhere else: INTRODUCTION TO NEURAL NETWORKS STRUCTURES OF NEURAL NETWORKS BUILDING A NEURAL NETWORK THE CONSTRUCTION OF ARTIFICIAL NEURONS THE BIOLOGICAL NEURONS MODEL HOW THEY WORK THE CAPABILITIES OF NEURAL NETWORK STRUCTURE TEACHING YOUR NETWORKS METHODS OF GATHERING INFORMATION ORGANIZING YOUR NETWORK USAGE OF MOMENTUM USING NEURAL NETWORKS USING NEURAL NETWORKS IN A PRACTICAL WAY THE CAPACITY OF A SINGLE NEURON Don't miss out on this new step by step guide to Neural Networks. All you need to do is scroll up and click on the BUY NOW button to learn all about it!
Author : Frank Millstein
Publisher : Frank Millstein
ISBN 13 :
Total Pages : 119 pages
Book Rating : 4./5 ( download)
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!
Author : Simeon Kostadinov
Publisher : Packt Publishing Ltd
ISBN 13 : 1789133661
Total Pages : 115 pages
Book Rating : 4.7/5 (891 download)
Download or read book Recurrent Neural Networks with Python Quick Start Guide written by Simeon Kostadinov and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)
Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.
Author : Bob Story
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781548960292
Total Pages : 56 pages
Book Rating : 4.9/5 (62 download)
Download or read book Neural Networks for Beginners written by Bob Story and published by Createspace Independent Publishing Platform. This book was released on 2017-07-15 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)
Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author : Basilio de Braganca Pereira
Publisher : CRC Press
ISBN 13 : 0429775547
Total Pages : 286 pages
Book Rating : 4.4/5 (297 download)
Download or read book Statistical Learning Using Neural Networks written by Basilio de Braganca Pereira and published by CRC Press. This book was released on 2020-08-25 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Author : Ahmed Fawzy Gad
Publisher : Academic Press
ISBN 13 : 0323909337
Total Pages : 300 pages
Book Rating : 4.3/5 (239 download)
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
Author : Basilio de Braganca Pereira
Publisher : CRC Press
ISBN 13 : 0429775555
Total Pages : 234 pages
Book Rating : 4.4/5 (297 download)
Download or read book Statistical Learning Using Neural Networks written by Basilio de Braganca Pereira and published by CRC Press. This book was released on 2020-09-01 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students. Key Features: Discusses applications in several research areas Covers a wide range of widely used statistical methodologies Includes Python code examples Gives numerous neural network models This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results. This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks.
Author : Prateek Joshi
Publisher : Packt Publishing Ltd
ISBN 13 : 1786469677
Total Pages : 437 pages
Book Rating : 4.7/5 (864 download)
Download or read book Artificial Intelligence with Python written by Prateek Joshi and published by Packt Publishing Ltd. This book was released on 2017-01-27 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : L D Knowings
Publisher :
ISBN 13 : 9781963790092
Total Pages : 0 pages
Book Rating : 4.7/5 (9 download)
Download or read book Building Neural Networks from Scratch with Python written by L D Knowings and published by . This book was released on 2024-02-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ready to throw your hat into the AI and machine learning ring? Get started right here, right now! Are you sick of these machine-learning guides that don't really teach you anything? Do you already know Python, but you're looking to expand your horizons and skills with the language? Do you want to dive into the amazing world of neural networks, but it just seems like it's... not for you? Artificial intelligence is progressing at a fantastic rate-every day, a new innovation hits the net, providing more and more opportunities for the advancement of society. In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing. But it just seems... complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out? Well, it just got easier. Machine learning and neural networking don't have to be complicated-with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed! In this all-encompassing guide to coding neural networks in Python, you'll uncover everything you need to go from zero to hero-transforming how you code and the scope of your knowledge right before your eyes. Here's just a portion of what you will discover in this guide: ● A comprehensive look at what a neural network is - including why you would use one and the benefits of including them in your repertoire ● All that pesky math dissuading you? Get right to the meat and potatoes of coding without all of those confusing equations getting you down ● Become a debugging master with these tips for handling code problems, maximizing your efficiency as a coder, and testing the data within your code ● Technological advancements galore! Learn how to keep up with all the latest trends in tech-and why doing so is important to you ● What in the world are layers and gradients? Detailed explanations of complex topics that will demystify neural networks, once and for all ● Dealing with underfitting, overfitting, and other oversights that many other resources overlook ● Several beginner-friendly neural network projects to put your newfound knowledge to the test And much more. Imagine a world where machine learning is more accessible, where neural networks and other complex topics are available to people just like you-people with a passion. Allowing for such technological advancements is going to truly change our world. It might seem hard, and you might be concerned based on other resources you've browsed-but this isn't an opportunity you can pass up on! By the end of this book, you'll have mastered neural networks confidently!
Author : Chris Ferrie
Publisher : Sourcebooks, Inc.
ISBN 13 : 149267382X
Total Pages : 26 pages
Book Rating : 4.4/5 (926 download)
Download or read book Neural Networks for Babies written by Chris Ferrie and published by Sourcebooks, Inc.. This book was released on 2019-03-01 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fans of Chris Ferrie's ABCs of Economics, ABCs of Space, and Organic Chemistry for Babies will love this introduction to neural networks for babies and toddlers! Help your future genius become the smartest baby in the room! It only takes a small spark to ignite a child's mind. Neural Networks for Babies by Chris Ferrie is a colorfully simple introduction to the study of how machines and computing systems are created in a way that was inspired by the biological neural networks in animal and human brains. With scientific and mathematical information from an expert, this installment of the Baby University board book series is the perfect book for enlightening the next generation of geniuses. After all, it's never too early to become a scientist! If you're looking for programming for babies, coding for babies, or more Baby University board books to surprise your little one, look no further! Neural Networks for Babies offers fun early learning for your little scientist!
Author :
Publisher :
ISBN 13 :
Total Pages : 334 pages
Book Rating : 4.:/5 (3 download)
Download or read book Resources in Education written by and published by . This book was released on 1998 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt:
