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Related lectures (29)

Page 3 of 3

Foundations of Deep Learning: Transformer Architecture Overview

Covers the foundational concepts of deep learning and the Transformer architecture, focusing on neural networks, attention mechanisms, and their applications in sequence modeling tasks.

Reinforcement Learning: Q-Learning

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Variations of SARSA: Expected SARSA and Q Learning

Explores expected SARSA and Q learning, two variations of the SARSA algorithm.

Continuous Reinforcement Learning: Advanced Machine Learning

Explores continuous-state reinforcement learning challenges, value function estimation, policy gradients, and Policy learning by Weighted Exploration.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Temporal difference learning

Covers Reinforcement Learning theory, Q-Learning, and multi-step horizons.

Prompting and Alignment

Explores prompting, alignment, and the capabilities of large language models for natural language processing tasks.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Theory of reinforcement learning: Grid examples

Explains the theory of reinforcement learning through grid examples and iterative Q-value updates.