Key Information

Tutor: Dr Andreas Joergensen
Course Level: Level 2
Course Credit: 1 credit
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.
Course Duration: 1-hour remote introduction (MS Teams) & 3 consecutive weekly 2-hour face to face teaching sessions.

Course Resources

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

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.

Dates & Booking Information

  • Thursday 07 November 2024, 10:00-11:00 (Pre-course information session - MS Teams), Thursday 14 November 2024 (Part 1), Thursday 21 November 2024 (Part 2) & Thursday 28 November 2024 (Part 3) 10:00-12:00, White City (In-Person Teaching)

  • Wednesday 29 January 2025, 14:00-15:00 (Pre-course information session - MS Teams), Wednesday 05 February 2025 (Part 1), Wednesday 12 February 2025 (Part 2) & Wednesday 19 February 2025 (Part 3) 14:00-16:00, South Kensington (In-Person Teaching)

To book your place, please follow the booking process advertised on the main programme page

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