About the course
This repository is for use with the Pearson Publishing live webinar "Machine
PyTorch." Versions of this material are used by other training
provided by David Mertz and KDM Training.
If you have attended one of the webinars using this material, I encourage you to complete the survey on it at: Machine Learning Webinar survey. As folks fill this out, we will fold back the updated answers into the dataset used in the lessons themselves.
Installing training materials
Before attending this course, please configure the environments you will need.
Within the repository, find the file
requirements.txt to install software
pip, or the file
environment.yml to install software using
$ conda env create -f environment.yml $ conda activate Pearson-PyTorch (Pearson-ML) $ jupyter notebook Outline.ipynb
$ pip install -r requirements.txt $ juypter notebook Outline.ipynb
PyTorch often works vastly faster when utilizing a CUDA GPU to perform training.
Students who wish to be able to follow along running the material on their own machines in real time, are advised to obtain access to a GPU machine while attending this webinar.
Numerous cloud services provide access to rented GPU instances are reasonable hourly costs. AWS EC2 instances are very well known, and can be leased with good GPU configurations. The author is very fond of a service called vast.ai (https://vast.ai/) that he will use during presentation of the webinar. Of course, if you have any moderately recent CUDA-enabled GPU on your home or work machine, you will be fine also.
GitHub:pytorch-examples, by Justin Johnson (written or PyTorch 0.4, but very clear conceptual summary)
(Video) Machine Learning with scikit-learn LiveLessons, by David Mertz
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron
Deep Learning with Python, by Francois Chollet
Introduction to Machine Learning with Python: A Guide for Data Scientists, by by Andreas C. Müller & Sarah Guido
Python Data Science Handbook: Essential Tools for Working with Data, by Jake VanderPlas