Concept

Hyperparameter (machine learning)

Related lectures (30)

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Neural Networks: Regularization & Optimization

Explores neural network regularization, optimization, and practical implementation tips.

Gradient Descent and Linear Regression

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

Transformers: Self-Attention and MLP

Explores transformers, emphasizing self-attention and MLP mechanisms for efficient sequence processing.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Decentralized Optimization

Explores decentralized optimization in machine learning, emphasizing robustness, privacy, and fairness in collaborative learning.

Machine Learning Fundamentals

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

Supervised Learning: Gradient Descent

Covers supervised learning with gradient descent algorithms and their statistical justification.

Deep Learning: Designing Neural Network Models

Covers the design and optimization of neural network models in deep learning.

Crash course on Deep Learning

Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Policy Gradient Methods: Reinforcement Learning Techniques

Provides an overview of policy gradient methods in reinforcement learning, focusing on the log-likelihood trick and the transition from batch to online learning.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

How to change the policy with a gradient method

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

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Reinforcement Learning for Pacman

Covers the application of reinforcement learning to teach Pacman to play autonomously by trial and error.

Deep Reinforcement Learning: Mini-Batches and Policy Methods

Discusses deep reinforcement learning methods, focusing on mini-batches and the implications of on-policy and off-policy training techniques.