Training complex deep learning models with large datasets takes along time. In this course, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.

Learn the theoretical foundations of Deep Learning through practical Python code.

Extend your knowledge of the core techniques of computational linguistics by working through case-studies and visualizing their results.

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using thepopular Keras library.

Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.

This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.

In this capstone project, you'll use either Keras or PyTorch to develop, train, and test a Deep Learning model. Load and preprocess data for a real problem, build the model and then validate it.