Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques

Key Features

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Discover practical recipes to overcome various challenges faced while building computer vision models

Enable machines to gain a human level understanding to recognize and analyze digital images and videos

Book Description

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book bs by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

What you will learn

Understand how to detect objects using state-of-the-art models such as YOLOv3

Use AutoML to predict gender and age from images

Snt images using different approaches such as FCNs and generative models

Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test augmentation

Enable machines to recognize people's emotions in videos and real- streams

Access and reuse advanced TensorFlow Hub models to perform image classification and object detection

Generate captions for images using CNNs and RNNs

Who this book is for

This book is for computer vision developers and eeers, as well as deep learning practitioners looking for go-to solutions to various problems that commonly arise in computer vision. You will discover how to employ modern machine learning (ML) techniques and deep learning architectures to perform a plethora of computer vision tasks. Basic knowledge of Python programming and computer vision is required.

Table of Contents

Getting Started with TensorFlow 2.x for Computer Vision

Perfog Image Classification

Harnessing the Power of Pre-Trained Networks with Transfer Learning

Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution

Reducing Noise with Autoencoders

Generative Models and Adversarial Attacks

Captioning Images with CNNs and RNNs

Fine-Grained Understanding of Images through Sntation

Localizing Elements in Images with Object Detection

Applying the Power of Deep Learning to Videos

Streamlining Network Implementation with AutoML

Boosting Performance



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