Long short-term memory

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Related lectures (32)

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.

Deep Learning: Backward Propagation and Vanishing Gradient

Delves into backward propagation in deep learning, addressing the vanishing gradient challenge and the need for effective hidden units.

Attention and Cognitive Load

Explores attention, arousal, cognitive load, and learning strategies.

Advanced Student Modeling

Explores advanced student modeling techniques, neural networks, LSTM networks, and Deep Knowledge Tracing.

Neural Network Syntactic Parsing

Explores neural network-based syntactic parsing with iterative refinement for improved accuracy on complex graphs.

From average to online learning

Covers the transition from a Monte Carlo approximation of the average to deriving batch and online update rules.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

How to change the policy with a gradient method

Explains optimizing total reward by changing the policy with gradient methods.

Vanishing Gradient Problem: Deep Learning

Discusses the vanishing gradient problem in deep neural networks and its solutions.

Introduction to policy gradient

Introduces policy gradients, optimizing rewards by associating actions with observations.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

How Memory Functions

Delves into sensory, short-term, and long-term memory processes, including iconic and echoic memories, rehearsal techniques, and the working memory model.

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