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The TensorFlow Workshop

The TensorFlow Workshop

By : Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone
4.6 (25)
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The TensorFlow Workshop

The TensorFlow Workshop

4.6 (25)
By: Matthew Moocarme, Abhranshu Bagchi, Anthony So , Maddalone

Overview of this book

Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
Table of Contents (13 chapters)
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Preface

Implementing Custom Layers

Previously, you looked at implementing your own custom loss function with either the TensorFlow functional API or the subclassing approach. These concepts can also be applied to creating custom layers for a deep learning model. In this section, you will build a ResNet module from scratch.

Introduction to ResNet Blocks

Residual neural network, or ResNet, was first proposed by Kaiming He in his paper Deep Residual Learning for Image Recognition in 2015. He introduced a new concept called a residual block that tackles the problem of vanishing gradients, which limits the ability of training very deep networks (with a lot of layers).

A residual block is composed of multiple layers. But instead of having a single path where each layer is stacked and executed sequentially, a residual block contains two different paths. The first path has two different convolution layers. The second path, called the skip connection, takes the input and forwards it to the...

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