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==3.8.5

• Activate the conda environment: conda activate pytorch101

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

• Install other packages: conda install scikit-learn==0.23.2 matplotlib==3.3.2 jupyter==1.0.0 ipywidgets==7.5.1 plotly==4.14.3 -c anaconda

• Install torchviz: pip install torchviz

• Clone this repo: git clone https://github.com/dvgodoy/PyTorch101_ODSC_Europe2021.git

• Start Jupyter: jupyter notebook