Pearson – Machine Learning With Pytorch-iLLiTERATE

Pearson – Machine Learning With Pytorch-iLLiTERATE
English | Size: 12.61 GB
Category: Tutorial


6+ Hours of Video Instruction
Learn the main concepts and techniques used in modern machine learning and deep neural networks through numerous examples written in PyTorch

Overview

This course begins with the basic concepts of machine and deep learning. Subsequently, you gain a reasonable familiarity with the main features of PyTorch and learn how it can be applied to some popular problem domains.

About the Instructor

David Mertz has been involved with the Python community for 20 years, with data science (under various earlier names), and with machine learning (since way back when it was more likely to be called “artificial intelligence”). He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups.

He has also written quite a bit about Python: the column “Charming Python” for IBM developerWorks, for many years; the book Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them.

Skill Level
Intermediate

Learn How To

Apply various machine and deep learning techniques
Understand the difference between various machine and deep learning libraries
Create classifiers
Enhance an existing classifier
Who Should Take This Course
Programmers and statisticians interested in using Python and the PyTorch library to implement machine learning

Course Requirements
Programming experience

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