$ grep --tag

#Gradient Descent

// 3 posts found

[MMD]·03/06/2026·16min

Calculus Coding Assignments: Implementing Optimization Algorithms with NumPy

Hands-on coding assignments implementing numerical differentiation, gradient descent, perceptron backpropagation, and Newton's method from scratch with NumPy. Each problem includes step-by-step hints and complete solution code.

#Calculus#Python#NumPy#Coding Assignment#Gradient Descent

[MMD]·03/06/2026·13min

ML Calculus Chapter 3: Gradient Descent and Neural Network Optimization

A deep dive into the principles of gradient descent and learning rate, perceptron regression and classification, backpropagation, and Newton's method with the Hessian — the core concepts of neural network optimization.

#Calculus#Machine Learning#Gradient Descent#Backpropagation#Neural Network

[MMD]·03/06/2026·11min

ML Calculus Chapter 2: Optimization, Partial Derivatives, and Gradients

A deep dive into the core of machine learning optimization: minimizing loss functions, differentiating squared and log loss, understanding partial derivatives and gradients, and using the gradient to find minima.

#Calculus#Machine Learning#Optimization#Gradient Descent#Gradient

// categories

// tags

#Gradient Descent

Calculus Coding Assignments: Implementing Optimization Algorithms with NumPy

ML Calculus Chapter 3: Gradient Descent and Neural Network Optimization

ML Calculus Chapter 2: Optimization, Partial Derivatives, and Gradients