Deep Learning with Python

Following on from the Introduction to Machine Learning course, this series of hands-on workshops will get you started with Deep Learning in Python, using the popular PyTorch library. In particular, the course will focus on so-called convolutional neural networks (CNN) for computer vision.

Syllabus:

• Neural network architectures
• Training and optimisation of neural networks
• Convolutional neural networks (CNN)
• Building and evaluating deep learning models in PyTorch

This course is open to Research Degree Students, Postdocs & Research Fellows. Limited spaces available for wider Imperial community.

Learning Outcomes:

By the end of the course, you will be better able to:

• Explain the basic terminology and concepts of deep learning methods.
• Summarise applications of different neural network architectures, including CNN.
• Understand the implementation of neural networks in PyTorch, including their training and testing.
• Apply a range of neural network architectures in PyTorch, including CNN, to data.
• Assess the performance of a range of neural networks in PyTorch, including CNN.

Prerequisites

Introduction to Machine Learning (or equivalent prior learning). Intermediate Python programming skills (numpy, matplotlib, functions, classes). Example code will be provided in each tutorial. No prior knowledge of PyTorch or Deep Learning is required

How to book

• Early Career Researchers (Research Degree Students, Postdocs, Research Fellows) should book via Inkpath using your Imperial Single-Sign-On.
• Wider College Community - All other members of the Imperial community, should book here.

Please ensure you have read and understood ECRI’s cancellation policy before booking.