Handwritten Digit Project Basics: Understanding train.csv, test.csv, and Labels
A dataset-first walkthrough of the handwritten digit project, covering train.csv, test.csv, labels, and the 784-feature image representation.
AI Learning Project
Handwritten Digit Softmax Classifier in C: From 784 Pixels to submission.csv
A practical walkthrough of the C softmax classifier for handwritten digits, including logits, softmax probabilities, gradient updates, and CSV submission export.
Handwritten Digit Playground Notes: Bringing an Offline Classifier into the Browser
Notes on the browser-side handwritten digit module, including the compact softmax demo model, sample browsing, drawing input, and probability inspection.
Neural Network Basics: From Perceptrons to Multi-Layer Networks
An accessible introduction to neurons, weights, bias, activation functions, forward propagation, backpropagation, and training loops.
Python AI Mini Practice: A Classification Task with scikit-learn
A small end-to-end classification project using a built-in scikit-learn practice dataset, covering data loading, splitting, scaling, training, evaluation, and experiment notes.
AI Basics Learning Roadmap: AI, Machine Learning, and Deep Learning
A practical roadmap for programmers learning AI foundations, including the relationship between AI, machine learning, and deep learning.
Machine Learning Workflow: From Data and Features to Predictions
An engineering-oriented walkthrough of the machine learning workflow, from problem definition and features to prediction and evaluation.
Model Training and Evaluation: Loss, Overfitting, and Accuracy
A beginner-friendly explanation of model parameters, loss functions, gradient descent, overfitting, and classification metrics.