Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
