Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.
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.
