Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Neural Networks: Multilayer PerceptronsCovers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Convolutional Neural NetworksCovers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.
Pytorch Intro: MNIST and DigitsCovers Pytorch basics with MNIST and Digits datasets, focusing on training neural networks for handwritten digit recognition.
Non-Conceptual Knowledge SystemsDelves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Neural Networks OptimizationExplores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.