Deep Learning with Python and PyTorch

Deep Learning with Python and PyTorch by edX is part of a two-part series on developing Deep Learning models using Pytorch. The first course in the series is titled ‘PyTorch Basics for Machine Learning’, and it covers the basics of PyTorch. Hence, you must have completed the first course to understand this programme thoroughly.

Furthermore, the Deep Learning with Python and PyTorch course syllabus aims to teach how to build deep neural networks. You will learn methods such as initialisation, dropout, batch normalisation and different types of optimisers. The programme will further explore concepts like data whitening, deep autoencoders, shallow autoencoders, autoencoder applications, etc. After you are done with all the modules, you can test your skills through the final independent project.

The Deep Learning with Python and PyTorch certification falls under the subject of Data Analysis and Statistics. This is a six-week-long intermediate-level course that will not explain things from scratch. Thus, you must have prior knowledge and interest in the field of machine learning. There are two options to join the programme, namely, verified and free audit. In the verified mode, the learners will have unlimited access to the self-paced course and in the free mode, they will have access only for a limited period.