$ grep --tag

#Backpropagation

// 3 posts found

[MMD]·03/06/2026·10min

Calculus Concept Practice: Problems You Encounter in ML Optimization

A collection of concept exercises applying differentiation, loss function optimization, gradient descent, and backpropagation to real-world machine learning scenarios. Each problem is grounded in situations you commonly encounter when training models in practice.

#Calculus#Machine Learning#Assignment#Optimization#Backpropagation

[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

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#Backpropagation

Calculus Concept Practice: Problems You Encounter in ML Optimization

Calculus Coding Assignments: Implementing Optimization Algorithms with NumPy

ML Calculus Chapter 3: Gradient Descent and Neural Network Optimization