Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
