Deep Learning
Department of Electronic & Electrical Engineering • Trinity College Dublin
Module Overview
An intensive graduate-level introduction to deep learning architectures and their practical applications in signal processing, audio, computer vision, and natural language processing. The curriculum emphasizes first-principles mathematical derivations alongside high-performance PyTorch implementations and distributed GPU training.
Core Topics Covered
Foundations & Optimisation Dynamics
Computational graphs, backpropagation mechanics, loss surfaces, gradient descent dynamics, and regularisation strategies (BatchNorm, LayerNorm, Dropout).
Convolutional Architectures & Computer Vision
Spatial convolutions, receptive fields, feature hierarchies, residual connections (ResNet, ConvNeXt), and dense spatial representations.
Sequence Modelling & Attention Mechanics
Recurrent architectures (RNNs, LSTMs), sequence-to-sequence translation, and scaled dot-product self-attention.
Transformers & Generative Modelling
Transformer encoder-decoder architectures, Vision Transformers (ViT), generative adversarial networks (GANs), and diffusion probabilistic models.
Pedagogy & Lab Infrastructure
Following a mastery-learning framework, the course pairs theoretical lectures with a continuous autograded laboratory system. Students build deep learning models from scratch in Colab notebooks and receive rapid, automated performance verification upon submitting code to GitHub Classroom.
The entire curriculum is accompanied by comprehensive open courseware and interactive lecture notes authored specifically for the course, available publicly to students and researchers worldwide.