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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

By : Galeone
3.7 (7)
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Hands-On Neural Networks with TensorFlow 2.0

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)
By: Galeone

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)
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1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Classification and localization

An architecture like the one defined so far that has no information about the class of the object it's localizing is called a region proposal.

It is possible to perform object detection and localization using a single neural network. In fact, there is nothing stopping us adding a second head on top of the feature extractor and training it to classify the image and at the same time training the regression head to regress the bounding box coordinates.

Solving multiple tasks at the same time is the goal of multitask learning.

Multitask learning

Rich Caruna defines multi-task learning in his paper Multi-task learning (1997):

"Multitask Learning is an approach to inductive transfer that...

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