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Foundations of Deep Learning, 3-day course

Course aims

This 3-day course aims to give developers/technical personnel a solid grounding in the field of deep learning, assuming no prior knowledge of the field. Regular practical sessions are combined with lecture-style content to consolidate the course material. By the end of the course you will understand the concepts underlying neural networks and deep learning, how these models are designed and trained, and gain an intuition of the knowledge encoded in these networks. You will also learn the basics of the Tensorflow framework and be able to design, build, train and apply your own network models.

Summary of syllabus

  • Introduction to artificial intelligence (AI), machine learning (ML) and deep learning (DL). Basic important concepts underpinning ML and DL.

  • Mathematical prerequisites review.

  • Fundamentals of neural networks: distributed representations, concept hierarchy. Layer transformations, loss functions, network training and optimisers.

  • Feedforward networks / multilayer perceptrons.

  • Network design and initialisation: Xavier initialisation, batch normalisation, dropout layers, weight and layer normalisation.

  • Convolutional neural networks. Convolutional and pooling operations, strides, padding, transposed convolutions.

  • Analysis, interpretation and manipulation of trained neural networks.

  • Practical sessions to practise and consolidate course material. These sessions comprise an introduction to the Tensorflow deep learning framework to implement many of the concepts covered.


The FeedForward AI Academy programmes are led by Dr Kevin Webster, Honorary Research Fellow in Mathematics at Imperial College. Kevin recently completed teaching the graduate level course on Deep Learning in the mathematics department at Imperial College London in Autumn 2018.

Who is this course for? 

This course presumes pre-existing technical proficiency. You might be a web developer, backend developer or data scientist interested in expanding your skills to include deep learning.

It will be assumed you have:

  • Basic knowledge of Git, Object Oriented Programming and the Python programming language

  • Your own laptop to use during the session with Tensorflow, numpy & scipy pre-installed. We recommend anaconda distribution for ease of installation.

  • Understanding of core mathematical concepts as listed in Mathematics for Foundations of Machine Learning and Deep Learning Courses. These topics will be briefly refreshed but not taught from scratch. If you are not confident with these topics, we suggest you take the mathematics course first.

Dates, times & location

Dates & times: Three-day course running from Wed 13 to Fri 15 Feb 2019, 09.30 - 16.30.

Location: Central London, UK with easy access to a main tube station - the exact venue will be confirmed in the near future. 

If you have any questions about the course, please email