Deep Learning In The Browser

Deep Learning In The Browser Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Deep Learning In The Browser book. This book definitely worth reading, it is an incredibly well-written.

Deep Learning with JavaScript

Stanley Bileschi,Eric Nielsen,Shanqing Cai
Deep Learning with JavaScript Book PDF

Author : Stanley Bileschi,Eric Nielsen,Shanqing Cai
Publisher : Simon and Schuster
Page : 826 pages
File Size : 54,6 Mb
Release : 2020-01-24
Category : Computers
ISBN : 9781638351542

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Deep Learning with JavaScript by Stanley Bileschi,Eric Nielsen,Shanqing Cai Pdf

Summary Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. Foreword by Nikhil Thorat and Daniel Smilkov. About the technology Running deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack. About the book In Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation. What's inside - Image and language processing in the browser - Tuning ML models with client-side data - Text and image creation with generative deep learning - Source code samples to test and modify About the reader For JavaScript programmers interested in deep learning. About the author Shanging Cai, Stanley Bileschi and Eric D. Nielsen are software engineers with experience on the Google Brain team, and were crucial to the development of the high-level API of TensorFlow.js. This book is based in part on the classic, Deep Learning with Python by François Chollet. TOC: PART 1 - MOTIVATION AND BASIC CONCEPTS 1 • Deep learning and JavaScript PART 2 - A GENTLE INTRODUCTION TO TENSORFLOW.JS 2 • Getting started: Simple linear regression in TensorFlow.js 3 • Adding nonlinearity: Beyond weighted sums 4 • Recognizing images and sounds using convnets 5 • Transfer learning: Reusing pretrained neural networks PART 3 - ADVANCED DEEP LEARNING WITH TENSORFLOW.JS 6 • Working with data 7 • Visualizing data and models 8 • Underfitting, overfitting, and the universal workflow of machine learning 9 • Deep learning for sequences and text 10 • Generative deep learning 11 • Basics of deep reinforcement learning PART 4 - SUMMARY AND CLOSING WORDS 12 • Testing, optimizing, and deploying models 13 • Summary, conclusions, and beyond

Hands-On Python Deep Learning for the Web

Anubhav Singh,Sayak Paul
Hands On Python Deep Learning for the Web Book PDF

Author : Anubhav Singh,Sayak Paul
Publisher : Packt Publishing Ltd
Page : 390 pages
File Size : 54,8 Mb
Release : 2020-05-15
Category : Computers
ISBN : 9781789953794

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Hands-On Python Deep Learning for the Web by Anubhav Singh,Sayak Paul Pdf

Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.

Deep Learning in the Browser

Xavier Bourry,Kai Sasaki,Christoph Körner,Reiichiro Nakano
Deep Learning in the Browser Book PDF

Author : Xavier Bourry,Kai Sasaki,Christoph Körner,Reiichiro Nakano
Publisher : Unknown
Page : 128 pages
File Size : 52,5 Mb
Release : 2018
Category : Artificial intelligence
ISBN : OCLC:1055162391

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Deep Learning in the Browser by Xavier Bourry,Kai Sasaki,Christoph Körner,Reiichiro Nakano Pdf

This book covers the crossroads of web development and deep learning. Both technologies are beginning to meet, and this honeymoon will produce new fantastic applications that you cannot even imagine yet. In this book you will see how to concretely use the main JavaScript deep learning frameworks and web programming in the browser with the capture of inputs and the WebGL implementation. Deep learning in the browser is currently at an embryonic stage, but this is the best time to bet on it before it becomes a giant, and this book will get you in on the action. Are you ready to embark on the adventure?

Deep Learning

Ian Goodfellow,Yoshua Bengio,Aaron Courville
Deep Learning Book PDF

Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Page : 801 pages
File Size : 46,9 Mb
Release : 2016-11-10
Category : Computers
ISBN : 9780262337373

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Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf

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.

Deep Learning for Coders with fastai and PyTorch

Jeremy Howard,Sylvain Gugger
Deep Learning for Coders with fastai and PyTorch Book PDF

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 49,5 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

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Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

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

Practical TensorFlow.js

Juan De Dios Santos Rivera
Practical TensorFlow js Book PDF

Author : Juan De Dios Santos Rivera
Publisher : Apress
Page : 303 pages
File Size : 51,8 Mb
Release : 2020-10-03
Category : Computers
ISBN : 1484262727

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Practical TensorFlow.js by Juan De Dios Santos Rivera Pdf

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.​js​ is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard​, ​ml5js​, ​tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.​js​ to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.

Practical Deep Learning for Cloud, Mobile, and Edge

Anirudh Koul,Siddha Ganju,Meher Kasam
Practical Deep Learning for Cloud Mobile and Edge Book PDF

Author : Anirudh Koul,Siddha Ganju,Meher Kasam
Publisher : O'Reilly Media
Page : 586 pages
File Size : 55,8 Mb
Release : 2019-10-14
Category : Computers
ISBN : 9781492034834

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Practical Deep Learning for Cloud, Mobile, and Edge by Anirudh Koul,Siddha Ganju,Meher Kasam Pdf

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

The Deep Learning Revolution

Terrence J. Sejnowski
The Deep Learning Revolution Book PDF

Author : Terrence J. Sejnowski
Publisher : MIT Press
Page : 354 pages
File Size : 45,7 Mb
Release : 2018-10-23
Category : Computers
ISBN : 9780262038034

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The Deep Learning Revolution by Terrence J. Sejnowski Pdf

How deep learning—from Google Translate to driverless cars to personal cognitive assistants—is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

Deep Learning with Javascript

Anonim
Deep Learning with Javascript Book PDF

Author : Anonim
Publisher : Independently Published
Page : 166 pages
File Size : 49,5 Mb
Release : 2020-06-29
Category : Electronic
ISBN : 9798660010767

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Deep Learning with Javascript by Anonim Pdf

Deep Learning with Javascript provides a beginners guide to getting started with neural networks. Through a hands-on approach the book provides a wide-range of interactive examples that the user is able to customize and expand to help them understand the concepts in a real-world sense. Using example-based learning and projects. The book has been formatted and designed with sample listings and support material, so whether or not you are currently an expert in Javascript and neural network development, actively working with an existing framework, or completely in the dark about this mysterious topic, this book has something for you. If you're an experienced developer, you'll find this book a light refresher to the subject, and if you're deciding whether or not to delve into web-based games, this book may help you make that significant decision. The book introduces core mathematic fundamentals in addition to projects and exercises such as feature detection, inverse kinematics, text extaction from handwritten sources and much more. The text is organised to guide the reader through the exciting topic of deep learning from a ground-up hands-on perspective. Organized around the browser-based language (Javascript) in combination with popular open source libraries (Synaptic), the book includes numerous simplified practical examples in the body of the text as well as technical explanations on limitations and engineering workarounds.

Grokking Deep Learning

Andrew W. Trask
Grokking Deep Learning Book PDF

Author : Andrew W. Trask
Publisher : Simon and Schuster
Page : 475 pages
File Size : 45,8 Mb
Release : 2019-01-23
Category : Computers
ISBN : 9781638357209

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Grokking Deep Learning by Andrew W. Trask Pdf

Summary Grokking 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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking 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 math-supporting 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. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide

Deep Learning in Practice

Mehdi Ghayoumi
Deep Learning in Practice Book PDF

Author : Mehdi Ghayoumi
Publisher : CRC Press
Page : 143 pages
File Size : 45,6 Mb
Release : 2021-12-01
Category : Business & Economics
ISBN : 9781000483390

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Deep Learning in Practice by Mehdi Ghayoumi Pdf

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students, as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project, step by step.

Deep Learning

Robert Hack
Deep Learning Book PDF

Author : Robert Hack
Publisher : Unknown
Page : 202 pages
File Size : 49,5 Mb
Release : 2020-04-05
Category : Electronic
ISBN : 9798630949479

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Deep Learning by Robert Hack Pdf

Everything You Need to Know About Deep LearningDo you want to know all about Deep Learning?Wondering what you need to get started with Deep Learning?You Are 1-Click Away From Knowing All About Deep Learning.Hello! Welcome to this guide to "The Ultimate Beginner's Guide To Artificial Intelligence And Neural Networks" An understanding of deep learning begins with a precise definition of terms. Otherwise, you have a hard time separating the media hype from the realities of what deep learning can actually provide. Deep learning is part of both AI and machine learning. To understand deep learning, you must begin at the outside - that is, you start with AI, and then work your way through machine learning, and then finally define deep learning. This book would help you through this process. Why study Deep Learning Has best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This includes speech, language, vision, playing games like Go etc. This isn't by a little bit, but by a significant amount. Reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice. Is an architecture that can be adapted to new problems relatively easily e.g. Vision, time series, language etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory etc. Feature engineering can be automatically executed inside Deep Learning model Can solve complex problems flexible to be adapted to new challenge in the future (or transfer learning can be easily applied) High automation. Deep learning library (Tensorflow, keras, or MATLAB...) can help users build a deep learning model in seconds (without the need of deep understanding) More precisely, the book will teach you: Introduction to Deep Learning History of Deep Learning Conceptual foundations Neural Networks: The Building Blocks of Deep Learning training deep networks Convolutional and Recurrent Neural Networks Learning Functions The Future of Deep Learning And so much more Frequently Asked QuestionsQ: Do I need special software or hardware to read eBooks? A: All you need is your PC, laptop or hand held device and the free Reader software. We offer eBooks in three different formats: PDF download, EPUB download and Online Reader. Our Online Reader requires no software other than an internet browser. For downloading, we will provide you with a link to download the appropriate Reader software free of charge when you make a purchase. Q: How to buy kindle eBook? A: You can purchase Kindle books at any time using a web browser. Visit Kindle Store to start browsing. To purchase Kindle books using your reading app: Tap the Store tab or Shop in Kindle Store. Browse or search for the Kindle titles you want to read. Select Buy Now. So, what are you waiting for? Buy now to join the millions of people already learning about Deep Learning!

Advanced Deep Learning with Python

Ivan Vasilev
Advanced Deep Learning with Python Book PDF

Author : Ivan Vasilev
Publisher : Packt Publishing Ltd
Page : 456 pages
File Size : 53,8 Mb
Release : 2019-12-12
Category : Computers
ISBN : 9781789952711

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Advanced Deep Learning with Python by Ivan Vasilev Pdf

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Deep Learning for Computer Vision

Rajalingappaa Shanmugamani
Deep Learning for Computer Vision Book PDF

Author : Rajalingappaa Shanmugamani
Publisher : Packt Publishing Ltd
Page : 304 pages
File Size : 44,6 Mb
Release : 2018-01-23
Category : Computers
ISBN : 9781788293358

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Deep Learning for Computer Vision by Rajalingappaa Shanmugamani Pdf

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

Dive Into Deep Learning

Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Dive Into Deep Learning Book PDF

Author : Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy
Publisher : Corwin Press
Page : 297 pages
File Size : 52,8 Mb
Release : 2019-07-15
Category : Education
ISBN : 9781544385402

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Dive Into Deep Learning by Joanne Quinn,Joanne McEachen,Michael Fullan,Mag Gardner,Max Drummy Pdf

The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.