A Practical Guide To Neural Networks

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A Practical Guide To Neural Nets

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Author : Marilyn McCord Nelson
language : en
Publisher: Addison Wesley Publishing Company
Release Date : 1994

A Practical Guide To Neural Nets written by Marilyn McCord Nelson and has been published by Addison Wesley Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Computers categories.

Based on a course given to internal managers at Texas Instruments, this book is an introduction to neural nets for computer science, artificial intelligence and R & D professionals, as well as MIS or DP managers.

A Practical Guide To Neural Networks

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Author : Marilyn McCord Nelson
language : en
Publisher:
Release Date :

A Practical Guide To Neural Networks written by Marilyn McCord Nelson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

A Practical Guide To Neural Nets

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Author : Marilyn McCord Nelson
language : en
Publisher: Prentice Hall
Release Date : 1991

A Practical Guide To Neural Nets written by Marilyn McCord Nelson and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Computers categories.

Neural Networks

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Author : Steven Cooper
language : en
Publisher: Roland Bind
Release Date : 2018-11-06

Neural Networks written by Steven Cooper and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-06 with Computers categories.

☆★The Best Neural Networks Book for Beginners★☆ If you are looking for a complete beginners guide to learn neural networks with examples, in just a few hours, then you need to continue reading. Have you noticed the increasing prevalence of software that tries to learn from you? More and more, we are interacting with machines and platforms that try to predict what we are looking for. From movie and television show recommendations on Netflix based on your taste to the keyboard on your smartphone trying to predict and recommend the next word you may want to type, it's becoming obvious that machine learning will definitely be part of our future. If you are interested in learning more about the computer programs of tomorrow then, Understanding Neural Networks – A Practical Guide for Understanding and Programming Neural Networks and Useful Insights for Inspiring Reinvention is the book you have been waiting for. ★★ Grab your copy today and learn ★★ ♦ The history of neural networks and the way modern neural networks work ♦ How deep learning works ♦ The different types of neural networks ♦ The ability to explain a neural network to others, while simultaneously being able to build on this knowledge without being COMPLETELY LOST ♦ How to build your own neural network! ♦ An effective technique for hacking into a neural network ♦ Some introductory advice for modifying parameters in the code-based environment ♦ And much more... You'll be an Einstein in no time! And even if you are already up to speed on the topic, this book has the power to illustrate what a neural network is in a way that is capable of inspiring new approaches and technical improvements. The world can't wait to see what you can do! Most of all, this book will feed the abstract reasoning region of your mind so that you are able to theorize and invent new types and styles of machine learning. So, what are you waiting for? Scroll up and click the buy now button to learn everything you need to know in no time!

Introduction To Deep Learning And Neural Networks With Pythontm

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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-25

Introduction To Deep Learning And Neural Networks With Pythontm written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Medical categories.

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

Deep Learning For Quantitative Finance

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Author : VINCENT. BISETTE
language : en
Publisher:
Release Date : 2025

Deep Learning For Quantitative Finance written by VINCENT. BISETTE and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.

Hands On Deep Learning With Go

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Author : Gareth Seneque
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-08-08

Hands On Deep Learning With Go written by Gareth Seneque 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 2019-08-08 with Computers categories.

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key FeaturesGain a practical understanding of deep learning using GolangBuild complex neural network models using Go libraries and GorgoniaTake your deep learning model from design to deployment with this handy guideBook Description Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems. What you will learnExplore the Go ecosystem of libraries and communities for deep learningGet to grips with Neural Networks, their history, and how they workDesign and implement Deep Neural Networks in GoGet a strong foundation of concepts such as Backpropagation and MomentumBuild Variational Autoencoders and Restricted Boltzmann Machines using GoBuild models with CUDA and benchmark CPU and GPU modelsWho this book is for This book is for data scientists, machine learning engineers, and AI developers who want to build state-of-the-art deep learning models using Go. Familiarity with basic machine learning concepts and Go programming is required to get the best out of this book.

Introduction To Deep Learning And Neural Networks With Pythont

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Author : Ahmed Fawzy Gad
language : en
Publisher: Academic Press
Release Date : 2020-11-26

Introduction To Deep Learning And Neural Networks With Pythont written by Ahmed Fawzy Gad and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Medical categories.

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.

Hands On Deep Learning With R

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Author : Michael Pawlus
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-24

Hands On Deep Learning With R written by Michael Pawlus 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 2020-04-24 with Computers categories.

Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet Key FeaturesUnderstand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problemImprove models using parameter tuning, feature engineering, and ensemblingApply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domainsBook Description Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms. What you will learnDesign a feedforward neural network to see how the activation function computes an outputCreate an image recognition model using convolutional neural networks (CNNs)Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithmApply text cleaning techniques to remove uninformative text using NLPBuild, train, and evaluate a GAN model for face generationUnderstand the concept and implementation of reinforcement learning in RWho this book is for This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

1993 Ieee International Conference On Neural Networks San Francisco California March 28 April 1 1993

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Author :
language : en
Publisher:
Release Date : 1993

1993 Ieee International Conference On Neural Networks San Francisco California March 28 April 1 1993 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Neural circuitry categories.