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Book details
File Size: 21 MB
Format: epub
Print Length: 220 pages
Publisher: Packt Publishing; 1 edition (February 27, 2018)
Publication Date: February 27, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B0788635R7
Who This Book Is For:
This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
One stop guide to implementing award-winning, and cutting-edge CNN architectures.
Key Features
Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more
Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models
Book Description
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
This book starts with an overview of deep neural networks with the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.
Later we build upon the learning achieved to build advanced vision-related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
What you will learn
From CNN basic building blocks to advanced concepts understand practical areas they can be applied to
Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
Learn different algorithms that can be applied to Object Detection, and Instance Segmentation
Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy
Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more
Understand the working of generative adversarial networks and how it can create new, unseen images
Table of Contents
Deep Neural Networks – Overview
Introduction to Convolutional Neural Networks
Build Your First CNN and Performance Optimization
Popular CNN Model’s Architectures
Transfer Learning
Autoencoders for CNN
Object Detection with CNN
Generative Adversarial Network
Visual Attention Based CNN
Book details
File Size: 21 MB
Format: epub
Print Length: 220 pages
Publisher: Packt Publishing; 1 edition (February 27, 2018)
Publication Date: February 27, 2018
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B0788635R7
Who This Book Is For:
This book is for data scientists, machine learning and deep learning practitioners, Cognitive and Artificial Intelligence enthusiasts who want to move one step further in building Convolutional Neural Networks. Get hands-on experience with extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python programming language is expected.
One stop guide to implementing award-winning, and cutting-edge CNN architectures.
Key Features
Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
Implement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and more
Implement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention models
Book Description
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
This book starts with an overview of deep neural networks with the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.
Later we build upon the learning achieved to build advanced vision-related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
What you will learn
From CNN basic building blocks to advanced concepts understand practical areas they can be applied to
Build an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize it
Learn different algorithms that can be applied to Object Detection, and Instance Segmentation
Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracy
Understand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and more
Understand the working of generative adversarial networks and how it can create new, unseen images
Table of Contents
Deep Neural Networks – Overview
Introduction to Convolutional Neural Networks
Build Your First CNN and Performance Optimization
Popular CNN Model’s Architectures
Transfer Learning
Autoencoders for CNN
Object Detection with CNN
Generative Adversarial Network
Visual Attention Based CNN