Gradient Descent: Optimization

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Page 1 of 3

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Convergence Criteria: Necessary Conditions

Explains necessary conditions for convergence in optimization problems.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Coordinate Descent: Optimization Strategies

Explores coordinate descent optimization strategies, emphasizing simplicity in optimization through one-coordinate updates and discussing the implications of different approaches.

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.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Optimization Techniques: Gradient Descent and Convex Functions

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.