Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.
Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Covers the fundamentals of deep neural networks and splines, exploring their properties, implications, and applications in modern machine learning.
