Hyperparameter (machine learning)

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Related lectures (30)

Robustness and Diffusion Models

Explores robustness in GAN training, Gaussian algorithms, saddle points, and mixed Nash equilibrium.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Backpropagation and Neural Networks

Covers the backpropagation algorithm for training neural networks and the representation of functions in multilayer networks.

Kernel Methods: Understanding Overfitting and Model Selection

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Kernel Methods in Machine Learning: Kernel Regression and SVM

Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.

Policy Gradient Methods: Single Neuron Example

Covers policy gradient methods using a single neuron with binary output.

Deep Learning: No Free Lunch Theorem and Inductive Bias

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.

Policy Gradient Methods: Binary Actor Example

Introduces policy gradient methods using a simple example of a single neuron with binary output.

Applications of Machine Learning

Explores applications of machine learning, biased datasets, statistical distributions, and software demos.

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