Discover PyTorch for machine learning applications. Learn key concepts, build neural networks, and deploy models effectively. Ideal for beginners and experienced practitioners seeking practical skills in deep learning with PyTorch.
This training material is available under a CC BY-NC-ND 4.0 license.
Please contact, Erudio for hands-on, instructor-led, onsite or remote, training. Our email is hi@erudio.one.
Before attending this course, please configure the environments you will need. Within the repository, find the file requirements.txt
to install software using pip
, or the file environment.yml
to install software using conda
. I.e.:
$ conda env create -f environment.yml
$ conda activate erudio.pytorch
(erudio.pytorch) $ jupyter notebook PyTorch-00_Outline.ipynb
Or
$ pip install -r requirements.txt
$ juypter notebook PyTorch-00_Outline.ipynb
To share the environment, it is used:
conda env export --from-history | grep -v "^prefix: " > environment.yml
So only packages that are explicitly requested will be included, and without the prefix, which is the local folder where the package is installed.
After cloning this repository, you can restore the environment using:
conda env create -f environment.yml