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Hands-On Deep Learning with Go

Hands-On Deep Learning with Go

By : Seneque, Chua
3 (2)
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Hands-On Deep Learning with Go

Hands-On Deep Learning with Go

3 (2)
By: Seneque, Chua

Overview of this book

Go is an open source programming language designed by Google for handling large-scale projects efficiently. The Go ecosystem comprises some really powerful deep learning tools such as DQN and CUDA. With this book, you'll be able to use these tools to train and deploy scalable deep learning models from scratch. This deep learning book begins by introducing you to a variety of tools and libraries available in Go. It then takes you through building neural networks, including activation functions and the learning algorithms that make neural networks tick. In addition to this, you'll learn how to build advanced architectures such as autoencoders, restricted Boltzmann machines (RBMs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. You'll also understand how you can scale model deployments on the AWS cloud infrastructure for training and inference. By the end of this book, you'll have mastered the art of building, training, and deploying deep learning models in Go to solve real-world problems.
Table of Contents (15 chapters)
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Section 1: Deep Learning in Go, Neural Networks, and How to Train Them
6
Section 2: Implementing Deep Neural Network Architectures
11
Section 3: Pipeline, Deployment, and Beyond!

Summary

In this chapter, we had a look at the hardware side of deep learning. We also had a look at how CPUs and GPUs serve our computational needs. We also looked at how CUDA, NVIDIA's software, facilitates GPU-accelerated deep learning that is implemented in Gorgonia, and finally, we looked at how to build a model that uses the features implemented by CUDA Gorgonia.

In the next chapter, we will look into vanilla RNNs and the issues involved with RNNs. We will also learn about how to build an LSTM in Gorgonia as well.

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