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PCS5022 - Deep Learning

Machine Learning Basics

FeedForward Networks

BackPropagation, Weights Initialization, Learning Rate and Optimizers

Deep Learning

Regularization and Batch Normalization

Convolutional Networks, Building Blocks and Residual Networks

Data Augmentation, Dataset Distillation and Adversarial Attacks

Recurrent Networks

Transformers, Modern MLP Architectures and Foundation Models

Transfer Learning, Fine-Tuning and Knowledge Distillation

Self-supervised and Semi-supervised Learning

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