R Deep Learning Cookbook

R Deep Learning Cookbook by Dr. PKS Prakash English  4 Aug. 2017  ISBN: 1787121089  ASIN: B071NDMWN2  288 Pages  AZW3  6.91 MB Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book Master intricacies of R deep learning p
 9.61MB
R Deep Learning Cookbook 英文原版
20180821本书是介绍R语言做深度学习的，主要是涉及到Tensorflow，H2o，Mxnet三个深度学习的包，内容涉及的也非常全，既有MLP，也有CNN，RNN，最后还有增强学习（reinforcement learning）以及深度学习在自然语言处理方面的一些东西。
 14.44MB
R Deep Learning Cookbook 【PDF】
20170814"R Deep Learning Cookbook" ISBN: 1787121089  2017  PDF  282 pages  14.45 MB Key Features Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to setup deep learning models using CPU and GPU Book Description Deep Learning is the next big thing. It is a part of machine learning. Its favorable results in application with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. With the growth in Deep Learning, the inter relation between R and deep learning is growing tremendously as they are very compatible with each other in attaining the various results. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems. What you will learn Build deep learning models in different application areas using H20, MXnet. Analyzing a Deep boltzmann machine Setting up and Analysing Deep belief networks Generating a RNNRBM hybrid model for sequence generation Building supervised model using various machine learning algorithms Set up variants of basic convolution function Represent data using Autoencoders. Explore generative models available in Deep Learning. Implement Branching Program Machines for structured or sequential outputs Discover sequence modeling using Recurrent and Recursive nets Learn the steps involved in applying Deep Learning in text mining Train a deep learning model on a GPU
 23.93MB
deep learning cookbook
20190115深度学习入门的经典教材，也可当工具书使用，英文原版，非扫描
 9.13MB
Deep Learning Cookbook2018
20180724You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
 7.18MB
secureCRT5.13 绿色版
20140811secureCRT5.13 解压开就能直接用，免安装，纯绿色版本。
 13.11MB
Deep Learning Cookbook_ practical recipes to get started quickly
20180730Deep learning doesn't have to be intimidating. Until recently, this machinelearning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deeplearning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you're stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You'll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
 36.64MB
TensorFlow 1.x Deep Learning Cookbook 原版电子书+配套代码
20190114TensorFlow 1.x Deep Learning Cookbook 原版电子书+配套代码！
 27.50MB
TensorFlow 1.x Deep Learning Cookbook
20180320TensorFlow 1.x Deep Learning Cookbook
 2.81MB
Python Deep Learning Cookbook epub
20171129Python Deep Learning Cookbook 英文epub 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
 6.64MB
Python Deep Learning Cookbook 无水印pdf转化版
20171129Python Deep Learning Cookbook 英文无水印pdf pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
 40.72MB
TensorFlow+1.x+Deep+Learning+CookbookPackt+Publishing(2017).epub
20180222In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Qlearning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. You will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By the end of this book, you will be an expert in implementing deep learning techniques in growing realworld applications and research areas such as reinforcement learning, GANs, autoencoders and more.
 34.57MB
APACHE SPARK DEEP LEARNING COOKBOOK
20180718Set up a fully functional Spark environment Understand practical machine learning and deep learning concepts Apply builtin machine learning libraries within Spark Explore libraries that are compatible with TensorFlow and Keras Explore NLP models such as word2vec and TFIDF on Spark Organize dataframes for deep learning evaluation Apply testing and training modeling to ensure accuracy Access readily available code that may be reusable
 4.47MB
Deep Learning Cookbook Practical Recipes to Get Started Quickly
20181222Deep learning doesn’t have to be intimidating. Until recently, this machinelearning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deeplearning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections
 27.2MB
TensorFlow 1.x Deep Learning Cookbook配套代码
20180320TensorFlow 1.x Deep Learning Cookbook 配套代码 TensorFlow 1.x Deep Learning Cookbook 配套代码 TensorFlow 1.x Deep Learning Cookbook 配套代码
 88.74MB
源码+书TensorFlow 1.x Deep Learning Cookbook
20180729In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Qlearning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. Take the next step in implementing various common and notsocommon neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easytofollow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Handson recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AIdriven problems. Leverage different data sets such as MNIST, CIFAR10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve realworld use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipebased guide will take you from the realm of DNN theory to implementing them practically to solve the reallife problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Qlearnin... Read more...
 9.6MB
Deep_Learning_Cookbook
20180517Deep_Learning_Cookbook Deep_Learning_Cookbook Deep_Learning_Cookbook Deep_Learning_Cookbook
 5.10MB
【2018新书】Deep Learning Cookbook
20180802Chapter 1 provides indepth information about how neural networks function, where to get data from, and how to preprocess that data to make it easier to consume. Chapter 2 is about getting stuck and what to do about it. Neural nets are notoriously hard to debug and the tips and tricks in this chapter on how to make them behave will come in handy when going through the more projectoriented recipes in the rest of the book. If you are impatient, you can skip this chapter and go back to it later when you do get stuck. Chapters 3 through 15 are grouped around media, starting with text rocessing, followed by image processing, and finally music processing in Chapter 15. Each chapter describes one project split into various recipes. Typically a chapter will start with a data acquisition recipe, followed by a few recipes that build toward the goal of the chapter and a recipe on data visualization. Chapter 16 is about using models in production. Running experiments in notebooks is great, but ultimately we want to share our results with actual users and get our models run on real servers or mobile devices. This chapter goes through the options.
 7.37MB
Keras Deep Learning Cookbook
20181201Keras Deep Learning Cookbook Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Commissioning Editor: Amey Varangaonkar Acquisition Editor: Karan Jain Content Development Editor: Karan Thakkar Technical Editor: Sagar Sawant Copy Editor: Safis Editing Project Coordinator: Nidhi Joshi Proofreader: Safis Editing Indexer: Pratik Shirodkar Graphics: Jisha Chirayil Production Coordinator: Aparna Bhagat First published: October 2018 Production reference: 1301018 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 9781788621755 www.packtpub.com
 11.15MB
Apache Spark 2.x Machine Learning Cookbook
20171115Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark.azw3

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