This will give you a massive head start over trying to figure out the API from official documentation alone. I have designed each code example to use best practices and to be standalone so that you can copy and paste it directly into your project and adapt it to your specific needs. You can circle back for more theory later. The best way to learn deep learning in python is by doing. For that, I recommend starting with this excellent book. The focus of this tutorial is on using the PyTorch API for common deep learning model development tasks we will not be diving into the math and theory of deep learning. Photo by Dimitry B., some rights reserved. PyTorch Tutorial – How to Develop Deep Learning Models How to develop PyTorch deep learning models for regression, classification, and predictive modeling tasks. The five-step life-cycle of PyTorch models and how to define, fit, and evaluate models.The difference between Torch and PyTorch and how to install and confirm PyTorch is working.In this tutorial, you will discover a step-by-step guide to developing deep learning models in PyTorch.Īfter completing this tutorial, you will know: Achieving this directly is challenging, although thankfully, the modern PyTorch API provides classes and idioms that allow you to easily develop a suite of deep learning models. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook.Īt its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Predictive modeling with deep learning is a skill that modern developers need to know.
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