PyTorch is defined as an open source machine learning library for Python. It is used for applications such as natural language processing. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.
Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.
PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.
What this PyTorch Deep Learning Book offers?
This book has been prepared for python developers who focus on research and development with machine learning algorithms along with natural language processing system. The aim of this tutorial is to completely describe all concepts of PyTorch and realworld examples of the same.
What you will learn:
Introduction
Installation
Neural Network Basics
Universal Workflow of Machine Learning
Machine Learning vs. Deep Learning
Implementing First Neural Network
Neural Networks to Functional Blocks
Terminologies
Loading Data
Linear Regression
Convolutional Neural Network
Recurrent Neural Network
Datasets
Introduction to Convents
Training a Convent from Scratch
Feature Extraction in Convents
Visualization of Convents
Processing with Convents
Word Embedding
Recursive Neural Networks
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