An Introduction to PyTorch Fundamentals
An Introduction to PyTorch Fundamentals


In this tutorial, we shall have a 30-minute overview of PyTorch followed by a 1 hour tutorial that you shall complete, with the help of the instructor. PyTorch is a python library for numerical computing, automatic differentiation and deep learning, supporting a fast GPU backend for high performance. PyTorch is often used to build neural networks and for gradient-based methods in machine learning.

The tutorial will take you through doing operations on PyTorch Tensors, building your own neural networks, training them on small datasets and interpreting the final results. We shall use Google Colab ( ), a free Cloud notebook service to run the tutorials.
Familiarity with Python is required, and familiarity with NumPy helps.


Soumith Chintala is a Researcher at Facebook AI Research, where he works on deep learning, reinforcement learning, generative image models, agents for video games and large-scale high-performance deep learning with major contributions to the Torch deep learning framework which is used by the major players in the A.I. Industry. His research on generative models has been quoted to be one of the major advances in A.I. in 2015. His work in the A.I. research and systems community on benchmarking and consolidating systems is well recognized and is often quoted by Intel, Nervana Systems, NVIDIA and other hardware and systems companies as an independent metric. He holds a Masters in CS from NYU, and spent time in Yann LeCun’s NYU lab building deep learning models for pedestrian detection, natural image OCR, depth-images among others.

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