COMSM0045 - Applied Deep Learning
Unit Information
Welcome to COMSM0045. The unit introduces the students to deep architectures for learning linear and non-linear transformations of big data towards tasks such as classification and regression. The unit paves the path from understanding the fundamentals of convolutional and recurrent neural networks through to training and optimisation as well as evaluation of learnt outcomes. The unit's approach is hands-on, focusing on the 'how-to' while covering the basic theoretical foundations. For further general information, see the syllabus for the unit.
Staff
| Dima Damen (DD) | Unit Director |
| Tilo Burghardt (TB) |
Teaching Assistants
Hazel Doughty (HD), Will Price (WP), Evangelos Kazakos (EK), Jonathan Munro (JoM), Jian Ma (JiM), Xinyu Yang (XY), Dan Whettam (DW), Adriano Fragomeni (AF)
Unit Materials
| Wks | Pre-Recorded Lectures | Friday Synch Session 10am-1pm | Labs |
| 0 |
Wk0 INTRODUCTION TO COMSM0045 (Video) |
GETTING STARTED: Register Individually on BlueCrystal4 (details see below) | |
| 1 |
Wk1 - LECTURE 1 BASICS OF ARTIFICIAL NEURAL NETWORKS (Live on Teams at 10am 09/10/20, Slides) (Introduction, Neural Networks, Perceptron, Cost Functions, Gradient Descent, Delta Rule, Deep Networks) Wk1 - LECTURE 2 TOWARDS TRAINING DEEP FORWARD NETWORKS (Live on Teams at 11am 09/10/20, Slides) (Network Representation, Computational Graphs, Reverse Auto-Differentiation) |
(no scheduled lab for week 1) RECAP WORKSHEETS: -Convolutions (Homework) -Lab0 - Python (Homework) | |
| 2 |
Wk2- LECTURE 3 BACKPROPAGATION ALGORITHM (Video, Slides) (The Backpropagation Algorithm in Full Detail, Activation Functions) Wk2 - LECTURE 4 OPTIMISATION TECHNIQUES (Video, Slides) (Stochastic Gradient Descent, Nesterov Momentum, RMSProp, Newton's Method, AdaGrad, Adam, Saddle Points) |
16/10/20,10am,Online - PRACTICAL 1 (Slides), (Video) Your first fully connected layer gradient descent stochastic gradient descent |
16/10/20, Online - 3hrs -BC4 Setup Lab 1 - Training your first Deep Neural Network |
| 3 | Wk3 - LECTURE 5 COST FUNCTIONS, REGULARISATION AND DEPTH (Video, Slides) (SoftMax, Cross Entropy, Hingeloss, L1 and L2 Regularisation, DropOut, DropConnect, Depth Considerations) |
23/10/20,10am,Online - PRACTICAL 2 (Slides), (Video) Your first convolutional connected layer |
23/10/20, Online - 3hr Lab 2 - Your First Convolutional Connected Network |
|
Wk3 - LECTURE 6 CONVOLUTIONAL NEURAL NETWORKS (Video Part 1, Video Part 2, Slides Part 1, Slides Part 2) (sharing parameters, conv layers, pooling, CNN architectures) |
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| 4 | - |
30/10/20,10am,Online -
PRACTICAL 3 (Slides), (Video) Error rate monitoring (training/validation/testing) Batch-based training Learning rate Batch normalisation Parameter intialisation |
30/10/20, Online - 3hr Lab 3 - Hyperparameters |
| 5 | - | 6/11/20, 10am, Online Continuation Lab | 6/11/20, Online - 3hr Lab Continuation |
| 6 | - |
13/11/20, 11am, Online - Practical 4 Intro (Slides) (Video) PRACTICAL 4 Data Augmentation Debugging strategies Dropout |
13/11/20, Online - 3hr Lab 4 Data Augmentation |
| 7 | Wk7 - LECTURE 7 RECURRENT NEURAL NETWORKS (temporal dependencies, RNN, bi-directional RNNs, encoder-decoder, LSTM, gated RNN) (Slides) |
20/11/20, 10am, Online Continuation Lab + First CW Lab |
20/11/20, Online - 3hr Lab Continuation + Project Start |
| 8 | - | 27/11/20, 10am [1 hour], Online - CW Q/A | - |
| 9 | - | 4/12/20, 10am [1 hour], Online - CW Q/A | - |
| 10 | CW Deadline Fri 11 Dec 13:00 (Blackboard Submission) | ||
| 11 | - | 18/12/20, 10am [1 hour], Online - Exam Q/A | - |
Assessment Details
All students in the unit are requested to study the paper: Pan et al (2016). Shallow and Deep Convolutional Networks for Saliency Prediction. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available on ArXivCoursework specs are now available at: COMSM0045-COURSEWORK-SPECS-2020
- Coursework: You are requested to re-implement the paper above, and provide your code as well as a final report. Coursework completed in groups (up to 3)
- Exam: In addition to the content of the unit, students are expected to read and study the paper above. 25% of the exam will involve this paper, and its relationship to topics you have studied in the unit.
Assessment Details - Exam
Open book 2 hours exam in January (online). See note above on additional reading for exam.
Github
All technical resources will be posted on the COMSM0045 ADL Github organisation. If you find any issues, please kindly raise an issue in the respective repository.
Textbook
Recommended Reading:
Goodfellow et al (2016). Deep Learning. MIT Press
Blue Crystal 4 Registration [only applicable for Bristol undergraduate students with corresponding email]
All students must apply online to register an account on BC4 for this unit. This also applies to students who already have accounts on BC4 for other units (e.g. HPC), in this case you must register again using the instructions below.
- Click on: https://www.acrc.bris.ac.uk/login-area/apply.cgi
- Enter your personal details
- Choose: "Join an existing project"
- Enter project code: COSC020582
- Keep Preferred log-in shell as bash
- Do not provide any additional information
Note that it takes up to 48 hours to enable your account on BC4.