Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
