Skip to content
Beginner workshop on PyTorch - ODSC London 2019 - Nov 20th
Jupyter Notebook Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
images adding image Nov 15, 2019
.gitignore
LICENSE
PyTorch101.ipynb
PyTorch101_Colab.ipynb
README.md
gradient_descent.py

README.md

PyTorch 101: building a model step-by-step

Learn the basics of building a PyTorch model using a structured, incremental and from first principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and how to make use of its capabilities: autograd, dynamic computation graph, model classes, data loaders and more.

The main goal of this session is to show you how PyTorch works: we will start with a simple and familiar example in Numpy and "torch" it! At the end of it, you should be able to understand PyTorch's key components and how to assemble them together into a working model.

We will use Google Colab and work our way together into building a complete model in PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object oriented programming.

Open it in Google Colab PyTorch101_Colab.ipynb.

If you'd rather use a local environment, please follow these steps (assuming you use Anaconda):

  • Install GraphViz: https://www.graphviz.org/

  • Create a conda environment: conda create -n pytorch101 pip conda python==3.6.8

  • Activate the conda environment: conda activate pytorch101

  • Install PyTorch: https://pytorch.org/get-started/locally/

  • Install other packages: conda install scikit-learn==0.21.3 matplotlib==3.1.1 jupyter==1.0.0 ipywidgets==7.5.1 plotly==4.1.1 -c anaconda

  • Install torchviz: pip install torchviz

  • Clone this repo: git clone https://github.com/dvgodoy/PyTorch101_ODSC_London2019.git

  • Start Jupyter: jupyter notebook

You can’t perform that action at this time.