Название: PyTorch. An Introduction Guide to Pytorch Deep Learning for Beginners, 2019 Edition Автор: Jim Smith
Издательство: Amazon Digital Services LLC
Год: 2019
Страниц: 69
Язык: английский
Формат: epub, pdf (conv)
Размер: 10.1 MB
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.
PyTorch makes ease in building an extremely complex neural network. This feature has quickly made it a go-to library. In research work, it gives a tough competition to TensorFlow. Inventors of PyTorch wants to make a highly imperative library which can easily run all the numerical computation, and finally, they invented PyTorch. There was a big challenge for Deep learning scientist, Machine learning developer, and Neural Network debuggers to run and test part of the code in real-time. PyTorch completes this challenge and allows them to run and test their code in real-time. So they don't have to wait to check whether it works or not.
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.
Before proceeding with this tutorial, you need knowledge of Python and Anaconda framework (commands used in Anaconda). Having knowledge of Artificial Intelligence concepts will be an added advantage.
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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