Deep Learning: Lecture Slides and Notebooks

A ten session lecture series covering the core architectures of deep learning, from convolutions to Transformers and generative models. Each session has slides, and the exercises are in the accompanying Jupyter notebooks.

• 01. Convolution and Pooling
• 02. Convolutional Neural Networks
• 03. Modern Computer Vision
• 04. RNNs, LSTM, and GRU
• 05. Word Embeddings
• 06. Neural Language Models
• 07. Sequence-to-Sequence
• 08. Transformers
• 09. GANs
• 10. Autoencoders