Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasksKey FeaturesTrain different kinds of deep learning model from scratch to solve specific problems in Computer VisionCombine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and moreIncludes tips on optimizing and improving the performance of your models under various constraintsBook DescriptionDeep 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 learnSet up an environment for deep learning with Python, TensorFlow, and KerasDefine and train a model for image and video classificationUse features from a pre-trained Convolutional Neural Network model for image retrievalUnderstand and implement object detection using the real-world Pedestrian Detection scenarioLearn about various problems in image captioning and how to overcome them by training images and text togetherImplement similarity matching and train a model for face recognitionUnderstand the concept of generative models and use them for image generationDeploy your deep learning models and optimize them for high performanceWho this book is forThis 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.Table of ContentsGetting StartedImage Classification Image Retrieval Object Detection Semantic Segmentation Similarity Learning Image CaptioningGenerative modelsVideo Classification Deployment \*\*About the AuthorRajalingappaa Shanmugamani is currently working as a Deep Learning Lead at SAP, Singapore. Previously, he has worked and consulted at various startups for developing computer vision products. He has a Masters from Indian Institute of Technology - Madras where his thesis was based on applications of computer vision in the manufacturing industry. He has published articles in peer-reviewed journals and conferences and applied for few patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

lgli/Deep Learning for Computer Vision_ Expert - Rajalingappaa Shanmugamani.epub

lgrsnf/Deep Learning for Computer Vision_ Expert - Rajalingappaa Shanmugamani.epub

zlib/Computers/Rajalingappaa Shanmugamani [Shanmugamani, Rajalingappaa]/Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks Using TensorFlow and Keras_3629135.epub

TensorFlow 1.x Deep Learning Cookbook : Take the Next Step in Implementing Various Common and Not-so-common Neural Networks with Tensorflow 1.x

TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python

Deep Learning with Keras : Get to Grips with the Basics of Keras to Implement Fast and Efficient Deep-learning Models

Библиотека Keras - инструмент глубокого обучения: реализация нейронных сетей с помощью библиотек Theano и TensorFlow

Deep learning with Keras : implementing deep learning models and neural networks with the power of Python

Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow

TensorFlow 1. X Deep Learning Cookbook

Антонио Джулли, Суджит Пал; пер. с англ. Слинкин А. А

Moore, Stephen;Shanmugamani, Rajalingappaa

Gulli, Antonio, Kapoor, Amita

Antonio Gulli; Amita Kapoor

Gulli, Antonio, Pal, Sujit

Antonio Gulli; Sujit Pal

Джулли, Антонио

Antonio Gullì

Golden Pleasure Books Ltd

Packt Publishing Limited

ДМК Пресс

Hamlyn

First published: january 2018, Birmingham, UK, 2018

United Kingdom and Ireland, United Kingdom

Place of publication not identified, 2018

Packt Publishing, Birmingham, UK, 2017

Москва, Russia, 2018

Dec 12, 2017

Jan 23, 2018

2018-01-23

2017-12-12

0

lg2288569

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Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.xAbout This BookSkill up and implement tricky neural networks using Google's TensorFlow 1.xAn easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environmentWho This Book Is ForThis 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 LearnInstall TensorFlow and use it for CPU and GPU operationsImplement DNNs and apply them to solve different AI-driven problems.Leverage different data sets such as MNIST, CIFAR-10, 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 problemsBuild single and multilayer perceptrons in TensorFlowImplement CNN and RNN in TensorFlow, and use it to solve real-world 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 DetailDeep 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 recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life 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 Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow.With a problem-solution approach, 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, CIFAR-10, 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 using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.Style and approachThis book consists of hands-on recipes where you'll deal with real-world problems.You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x.Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.

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.
Table of Contents
Introduction to Deep Learning
Image Classification
Image Retrieval
Object Detection
Semantic Segmentation
Similarity Learning
Generative Models
Image Captioning
Video Classification
Deployment
**

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks About This Book 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 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. 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 In Detail 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. Style and approach This book will teach advanced techniques for Computer Vision, applying the deep learning model in reference to various datasets. Downloading the example code for this ..

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on 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 AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, 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 real-world 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 recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life 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 Q-learnin ..

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Keras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Who This Book Is For If you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep-learning with Keras. A knowledge of Python is required for this book. What You Will Learn Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm Fine-tune a neural network to improve the quality of results Use deep learning for image and audio processing Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases Identify problems for which Recurrent Neural Network (RNN) solutions are suitable Explore the process required to implement Autoencoders Evolve a deep neural network using reinforcement learning In Detail This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks. Style and approach This book is an easy-to-follow guide full of examples and real-world applications to help you gain an in-depth understanding of Keras. This book will showcase more than twenty working Deep Neural Networks coded in Python using Keras. Downloading the example code for thi..

Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision, the science of manipulating and processing images. In this book, you will learn different techniques in deep learning to accomplish tasks related to object classification, object detection, image segmentation, captioning, ...

2018-11-20