Foundations of PyTorch

Learning models owing to its flexibility, ease-of-use and built-in support for optimized hardware such as GPUs

Using PyTorch, you can build complex deep learning models, while still using Python-native support for debugging and visualization

In this course, Foundations of PyTorch, you will gain the ability to leverage PyTorch support for dynamic computation graphs, and contrast that with other popular frameworks such as TensorFlow

First, you will learn the internals of neurons and neural networks, and see how activation functions, affine transformations, and layers come together inside a deep learning model

Next, you will discover how such a model is trained, that is, how the best values of model parameters are estimated

You will then see how gradient descent optimization is smartly implemented to optimize this process

You will understand the different types of differentiation that could be used in this process, and how PyTorch uses Autograd to implement reverse-mode auto-differentiation

You will work with different PyTorch constructs such as Tensors, Variables, and Gradients

Finally, you will explore how to build dynamic computation graphs in PyTorch

You will round out the course by contrasting this with the approaches used in TensorFlow, another leading deep learning framework which previously offered only static computation graphs, but has recently added support for dynamic computation graphs

When you’re finished with this course, you will have the skills and knowledge to move on to building deep learning models in PyTorch and harness the power of dynamic computation graphs