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Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.
Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.
Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.
Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.