Tensors are the basic building block of all of machine learning and deep learning. Specifically, we're going to cover: Topic This notebook deals with the basic building block of machine learning and deep learning, the tensor. Subsequent notebooks build upon knowledge from the previous one (numbering starts at 00, 01, 02 and goes to whatever it ends up going to). This course is broken down into different sections (notebooks).Įach notebook covers important ideas and concepts within PyTorch. What we're going to cover in this module ¶ So you can focus on manipulating data and writing algorithms and PyTorch will make sure it runs fast.Īnd if companies such as Tesla and Meta (Facebook) use it to build models they deploy to power hundreds of applications, drive thousands of cars and deliver content to billions of people, it's clearly capable on the development front too. PyTorch also helps take care of many things such as GPU acceleration (making your code run faster) behind the scenes. And as of February 2022, PyTorch is the most used deep learning framework on Papers With Code, a website for tracking machine learning research papers and the code repositories attached with them. Machine learning researchers love using PyTorch. PyTorch is also used in other industries such as agriculture to power computer vision on tractors. Many of the worlds largest technology companies such as Meta (Facebook), Tesla and Microsoft as well as artificial intelligence research companies such as OpenAI use PyTorch to power research and bring machine learning to their products.įor example, Andrej Karpathy (head of AI at Tesla) has given several talks ( PyTorch DevCon 2019, Tesla AI Day 2021) about how Tesla use PyTorch to power their self-driving computer vision models. PyTorch allows you to manipulate and process data and write machine learning algorithms using Python code. PyTorch is an open source machine learning and deep learning framework. PyTorch Fundamentals ¶ What is PyTorch? ¶ Putting tensors (and models) on the GPUĠ0. Running tensors on GPUs (and making faster computations)ģ. Reproducibility (trying to take the random out of random) Reshaping, stacking, squeezing and unsqueezing One of the most common errors in deep learning (shape errors)įinding the min, max, mean, sum, etc (aggregation)
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