Detecting and classifying objects in images is a challenging problem. So far, we have treated the issue of image classification on a simple level; in a real-life scenario, we are unlikely to have pictures containing just one object. In industrial environments, it is possible to set up cameras and mechanical supports to capture images of single objects. However, even in constrained environments, such as an industrial one, it is not always possible to have such a strict setup. Smartphone applications, automated guided vehicles, and, more generally, any real-life application that uses images captured in a non-controlled environment require the simultaneous localization and classification of several objects in the input images. Object detection is the process of localizing an object into an image by predicting the coordinates of a bounding box that...
Hands-On Neural Networks with TensorFlow 2.0
By :
Hands-On Neural Networks with TensorFlow 2.0
By:
Overview of this book
TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers.
This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub.
By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Preface
What is Machine Learning?
Neural Networks and Deep Learning
Section 2: TensorFlow Fundamentals
TensorFlow Graph Architecture
TensorFlow 2.0 Architecture
Efficient Data Input Pipelines and Estimator API
Section 3: The Application of Neural Networks
Image Classification Using TensorFlow Hub
Introduction to Object Detection
Semantic Segmentation and Custom Dataset Builder
Generative Adversarial Networks
Bringing a Model to Production
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