Description
Deep Learning with PyTorch Quick Start Guide is designed as an ideal entry point for developers and data scientists who want to harness the power of the popular PyTorch framework in their projects. It explains the principles of deep learning in simple terms and shows you how to set up a workspace for training complex neural networks in Python.
The book traces the development path from building the simplest linear models to more advanced architectures such as convolutional neural networks (CNN) for image processing and recurrent neural networks (RNN) for natural language processing. In addition, important topics such as hyperparameter optimization, distributed processing in multiprocessor environments, and the eventual deployment of models in a real environment are also thoroughly explored.
Book Features
- Clear, concise, and practical explanations of the core concepts and foundations of neural networks and deep learning.
- Complete tutorial on building and training advanced machine vision models (Image Classification) using CNN
- Using PyTorch in the field of natural language processing (NLP) and working with text models and LSTM networks
- Teaching advanced model optimization techniques and using transfer learning
- A practical guide to industrial deployments and preparing deep learning models for entry into the job market
Book specifications
- Publisher: Packt
- Instructor/Author: David Julian
- Number of pages: 150
- Number of chapters: 6
- Format: PDF
Headlines
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User Guide
Extract the file and run it with the appropriate software.
Download link
File(s) password: www.downloadly.ir
File size
5.8 MB
