Deep Learning Quick Reference

Packt出版社2018年出版的深度学习新书，高清原版pdf（不是由epub转化的）。Enjoy!
Deep Learning Quick Reference Copyright o 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 author, 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: Virai madhav Content Development Editor: Varun Sony Technical Editor: Dharmendra yadav Copy Editors: Safis Editing Project Coordinator: Manthan Patel Proofreader: Safis editing Indexer: Pratik shirodkar Graphics: Tania dutta Production Coordinator: Deepika Naik First published: March 2018 Production reference: 1070318 Published by packt Publishing ltd Livery place 35 Livery street Birmingham B3 2PB, UK ISBN9781788837996 www.pAcktpub.com To my wife Lana, whose love and support define the best epoch of my life To my son, William, who is likely disappointed that this book doesn t have more dragons in t To my mother, Sharon, and to the memory of my father, Bob, who taught me that determination and resilience matter more than intelligence Mapt mapt.lo Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website Why subscribe? Spend less time learning and more time coding with practical eBooks and Videos from over 4,000 industry professionals Improve your learning with skill plans built especially for you Get a free eBook or video every month mapt is fully searchable Copy and paste, print, and bookmark content PacktPub, com Did you know that packt offers e Book versions of every book published, with PDF and epubfilesavailableYoucanupgradetotheeboOkversionatwww.PacktPub.comandasa print book customer, you are entitled to a discount on the e Book copy. Get in touch with us at service@packtpub com for more details Atwww.packtpub.comyoucanalsoreadacollectionoffreetechnicalarticlessignupfora range of free newsletters and receive exclusive discounts and offers on packt books and eBooKs Foreword I first met Mike Bernico when we were two of the founding members of a new data science team at a fortune 50 company Then it was a head y time there wasn ' t such a thing as formal data science education, so we were all selftaught. We were a collection of adventurous people from diverse backgrounds, who identified and learned data science techniques because we needed them to solve the problems that we were interested in. We built a team with an optimistic hacker approachthe belief that we could find and apply techniques"from the wild"to build interesting, useful things It is in this practical, scrappy spirit that Mike wrote Deep learning Quick reference book Deep learning is frequently made out to be mysterious and difficult; however, in this guide, Mike breaks down major deep learning techniques, making them approachable and applicable. With this book, you (yes, you! )can quickly get started with using deep learning for your own projects in a variety of different modalities Mike has been practising data science since before the discipline was named, and he has been specifically teaching the topic to university students for 3 years. Prior to this, he spent many years as a working computer scientist with a specialization in networks and security and he also has a knack for engaging with people and communicating with nonspecialists He is currently the lead data scientist for a large financial services company where he designs systems for data science, builds machine learning models with direct applications and for research publications, mentors junior data scientists, and teaches stakeholders about data science. he knows his stuff With Deep learning quick reference book, you'll benefit from Mike's deep experience, humor, and downtoearth manner as you build example networks alongside him. After you complete Mike's book, you'l have the confidence and knowledge to understand and apply deep learning to the problems of your own devising for both fun and function Bon voyage, and good hacking! J Malia Andrus, Ph. D Data Scientist Seattle washington Contributors About the author Mike bernico is a Lead data Scientist at State Farm Mutual Insurance companies he also works as an adjunct for the University of illinois at Springfield, where he teaches Essentials of Data Science, and Advanced Neural Networks and Deep Learning. Mike earned his MSCS from the University of Illinois at Springfield. He's an advocate for open source software and the good it can bring to the world. As a lifelong learner with umpteen hobbies, Mike also enjoys cycling, travel photography, and wine making Id like to thank the very talented State Farm Data Scientists, current and past, for their friendship expertise, and encouragement Thanks to my technical reviewers for providing insight and assistance with this book Most importantly, I'd like to thank my wife, Lana, and my son, Will, for making time for this in our lives About the reviewer Vitor Bianchi Lanzetta has a master's degree in Applied Economics from the University of Sao Paulo, one of the most reputable universities in Latin America he has done a lot of research in economics using neural networks. He has also authored r Data Visualization Recipes, Packt Publishing. Vitor is very passionate about data science in general, and he walks the earth with a personal belief that he is just as cool as he is geek. He thinks that you will learn a lot from this book, and that Tensor Flow may be the greatest deep learning tool currently available Packt is searching for authors like you If you re interested in becoming an author for Packt, please visit authors. packtpub com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea Table of contents Preface Chapter 1: The Building Blocks of Deep Learning The deep neural network architectures Neurons The neuron linear function 67777 Neuron activation functions The loss and cost functions in deep learning The forward propagation process The back propagation function 12 Stochastic and minibatch gradient descents 12 Optimization algorithms for deep learning 13 Using momentum with gradient descent 13 The RMsProp algorithm 14 The Adam optimizer 14 Deep learning frameworks 15 What is TensorFlow? 15 What is Keras? 16 Popular alternatives to TensorFlow 16 GPU requirements for Tensor Flow and Keras 16 Installing nvidia cuda toolkit and cudNN Installing Python 19 Installing tensor Flow and Keras 20 Building datasets for deep learning 22 Bias and variance errors in deep learning 22 The train val and test datasets 23 Managing bias and variance in deep neural networks 24 KFold crossvalidation 24 Summary 25 Chapter 2: Using Deep Learning to Solve Regression Problems 26 Regression analysis and deep neural networks 26 Benefits of using a neural network for regression 27 Table of contents Drawbacks to consider when using a neural network for regression 28 Using deep neural networks for regression 28 How to plan a machine learning problem 28 Defining our example problem 29 Loading the dataset 29 Defining our cost function Building an MLP in Keras 31 Input layer shape 32 Hidden layer shape 32 Output layer shape 32 Neural network architecture 33 Training the Keras model 34 Measuring the performance of our model 35 Building a deep neural network in Keras 36 Measuring the deep neural network performance 37 Tuning the model hyperparameters 38 Saving and loading a trained Keras model 39 Summary 40 Chapter 3: Monitoring Network Training Using TensorBoard a brief overview of tensor board 41 Setting up TensorBoard 42 Installing tensor Board 42 How Tensor board talks to Keras/TensorFlow 43 Running Tensor Board 43 Connecting Keras to TensorBoard 44 Introducing Keras callbacks 44 Creating a Tensor Board callback 45 Using Tensor Board 48 Visualizing training 48 Visualizing network graphs 49 Visualizing a broken network Summary 52 Chapter 4: Using Deep Learning to Solve Binary Classification Problems 53 Binary classification and deep neural networks 54 [i]
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Deep learning quick reference : useful hacks for training and optimizing d n n
20181211由于DRM签名保护,可能无法转成其他格式 "Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book, deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later, the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Pythonbased deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks"Cover, page 4. Read more...
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Deep Learning Toolbox Reference
20201024Deep Learning Toolbox Reference, Deep Learning Toolbox Reference, Deep Learning Toolbox Reference
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20160412This book consists of six chapters, which can be grouped into three subjects. The first subject is Machine Learning and takes place in Chapter 1. Deep Learning stems from Machine Learning. This implies that if you want to understand the essence of Deep Learning, you have to know the philosophy behin
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R Deep Learning Cookbook
20170810R 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 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 Who This Book Is For Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful. What You Will Learn Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. Analyzing a Deep boltzmann machine Setting up and Analysing Deep belief networks 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. Discover sequence modeling using Recurrent nets Learn fundamentals of Reinforcement Leaning Learn the steps involved in applying Deep Learning in text mining Explore application of deep learning in signal processing Utilize Transfer learning for utilizing pretrained model Train a deep learning model on a GPU In Detail Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. 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 a 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. Style and approach Collection of handson recipes that would act as your alltime reference for your deep learning needs
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Grokking Deep Learning 正式版pdf
20190128Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its mathsupporting library, NumPy, you’ll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you’re done, you’ll be fully prepared to move on to mastering deep learning frameworks
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Deep Learning Illustrated:A Visual, Interactive Guide
19700106Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence "This book is a stunning achievement, written with precision and depth of understanding. It entertains you and gives you lots of interesting information at the same time. I could never imagine understanding and gaining scientific knowledge, namely 'Deep Learning' can be this much fun! Reading the book is a pleasure and I highly recommend it." —maryamkhakpour, O'Reilly Online Learning (Safari) Reviewer "This title is a great resource for those looking to understand deep learning. The illustrations are helpful and aid in cementing a richer understanding of the content, and the background context surrounding biological motivations for the tools and techniques enables a greater appreciation of the field. I enthusiastically recommend this book to any and all who are interested in the topic of deep learning." vincepetaccio, O'Reilly Online Learning (Safari) Reviewer Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely visual, intuitive, and accessible, and yet offers a comprehensive introduction to the discipline’s techniques and applications. Packed with fullcolor applications and easytofollow code, it sweeps away much of the complexity of building deep learning models, making the subject approachable and fun to learn. Worldclass instructor and practitioner Jon Krohn–with crucial material from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. He also offers a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He covers essential theory with as little mathematics as possible, preferring to illuminate concep
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Deep Learning 英文原版 （花书） MIT Press
201708312016 Deep Learning by MIT Press: Abstracts: 1 Introduction Part I: Applied Math and Machine Learning Basics 2 Linear Algebra 3 Probability and Information Theory 4 Numerical Computation 5 Machine Learning Basics Part II: Modern Practical Deep Networks 6 Deep Feedforward Networks 7 Regularization for Deep Learning 8 Optimization for Training Deep Models 9 Convolutional Networks 10 Sequence Modeling: Recurrent and Recursive Nets 11 Practical Methodology 12 Applications Part III: Deep Learning Research 13 Linear Factor Models 14 Autoencoders 15 Representation Learning 16 Structured Probabilistic Models for Deep Learning 17 Monte Carlo Methods 18 Confronting the Partition Function 19 Approximate Inference 20 Deep Generative Models
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tensorflow for deep learning.pdf
201710211. Introduction to Deep Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Machine Learning eats Computer Science 10 Deep Learning Primitives 11 Fully Connected Layer 11 Convolutional Layer 12 Recurrent Neural Network (RNN) Layers 13 Long ShortTerm Memory (LSTM) Cells 14 Deep Learning Zoo 14 LeNet 14 AlexNet 15 ResNet 16 Neural Captioning Model 16 Google Neural Machine Translation 17 One shot models 18 AlphaGo 19 Generative Adversarial Networks 20 Neural Turing Machines 21 Deep Learning Frameworks 22 Empirical Learning 25 2. Introduction toTensorfow Primitives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Introducing Tensors 27 Scalars, Vectors, and Matrices 28 Matrix Mathematics 31 Tensors 33 Tensors in physics. 35 Mathematical Asides 37 Basic Computations in Tensorflow 38 Initializing Constant Tensors. 38 Sampling Random Tensors 40 Tensor Addition and Scaling 40 Matrix Operations 41 Tensor types 42 Tensor Shape Manipulations 43 Introduction to Broadcasting 44 Imperative and Declarative Programming 45 Tensorflow Graphs 46 Tensorflow Sessions 46 Tensorflow Variables 47 Review 4
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deep learning
20180108'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.
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Deep Learning with Python: A Handson Introduction
20170418Nikhil Ketkar, "Deep Learning with Python: A Handson Introduction" English  ISBN: 1484227654  2017  143 pages  PDF  7 MB Discover the practical aspects of implementing deeplearning solutions using the rich Python ecosystem. This book bridges the gap between the academic stateoftheart and the industry stateofthepractice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical knowhow in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
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Deep Learning: A Practitioner's Approach
20170811Deep Learning: A Practitioner's Approach by Josh Patterson English  28 July 2017  ISBN: 1491914254  ASIN: B074D5YF1D  538 Pages  AZW3  7.15 MB Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This handson guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their opensource Deeplearning4j (DL4J) library for developing productionclass workflows. Through realworld examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
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Neural Networks and Deep Learning Deep Learning explained to your granny
20180704Neural Networks and Deep Learning: Deep Learning explained to your granny。老奶奶都能搞懂深度学习。。。不懂高深数学都木关系。
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Fundamentals of Deep Learning [EPUB 2017]
20170615Nikhil Buduma, Nicholas Locascio, "Fundamentals of Deep Learning: Designing NextGeneration Machine Intelligence Algorithms" ISBN: 1491925612  2017  EPUB  304 pages  16 MB With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing inhouse deep learning teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.
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Fundamentals of Deep Learning（完美英文版pdf）
20180402With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice and are actively growing inhouse deep learning teams. For the rest of us, deep learning is still a pretty complex and difficult subject to grasp. Research papers are filled to the brim with jargon, and scattered online tutorials do little to help build a strong intuition for why and how deep learning practitioners approach problems. Our goal is to bridge this gap.
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吴恩达老师深度学习课程(deeplearning.ai) 视频笔记Deeplearning_AndrewNg_v5.42.pdf
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