## Table of Contents - [Lecture 1 (Week 1)](slides_1.html): Introduction - [Lecture 2 (Week 1)](slides_1a.html): Pattern Recognition and Machine Learning - [Lecture 3 (Week 2)](slides_2.html): Artificial Neural Networks - [Lecture 4 (Week 3)](slides_3.html): Fully Connected Networks and PyTorch - [Lecture 5 (Week 4)](lecture_5.html): Neural Networks For Vision - [Lecture 6 (Week 5)](lecture_6.html): Modern ConvNets for Classification and Segmentation - [Lecture 7 (Week 6)](lecture_7.html): Generative models and Autoencoders - [Lecture 8 (Week 7)](lecture_8.html): Generative Adversarial Networks - [Lecture 9 (Week 7)](lecture_9.html): Recurrent Neural Networks - [Lecture 10 (Week 8)](lecture_10.html): Brain-Inspired Neural Networks - [Lecture 11 (Week 9)](lecture_11.html): Neural Networks for Natural Language Processing