Subjects
Computer Science
Algorithms, data structures, systems — build your CS foundation.
See all (33)
Backpropagation — How Neural Networks Learn
Backpropagation computes the gradients that let neural networks learn. See the chain rule, one clear example, common mistakes, and where it is used.
BFS — Breadth-First Search Algorithm Explained
Breadth-first search visits a graph level by level. Learn how BFS uses a queue and when it finds shortest paths in an unweighted graph.
Big O Notation — Time Complexity Explained Simply
Big O notation explains how runtime grows as input size grows. Learn the definition, intuition, one worked example, and common mistakes.
Binary Search — Algorithm, Time Complexity & Code
Learn how binary search works, when it applies, why it runs in O(log n), and how to trace one clear example.
Boolean Algebra — Laws, Theorems & Simplification
Learn what Boolean algebra is, how its main laws work, and how to simplify a logical expression with one clear example.
CNN — Convolutional Neural Network Architecture
Learn what a CNN is, how convolution layers detect patterns, one worked example, common mistakes, and where CNNs are used.
Convex Optimization — Concepts, Methods & Applications
Convex optimization means minimizing a convex function over a convex set. Learn the definition, one example, and why the guarantee matters.
Data Structures — Arrays, Linked Lists, Trees & Graphs
Learn what data structures are, when to use arrays, linked lists, trees, and graphs, and how to tell them apart quickly.
Database Normalization — 1NF, 2NF, and 3NF in One Sentence
Store each fact in exactly one place — learn the core idea of normalization by exploring it through interactive visualizations.
Decision Trees — Entropy, Gini & Random Forest
Learn how decision trees use entropy and Gini to choose splits, see one worked example, and understand when random forests help.
Design Patterns in Math
Learn what design patterns mean in math, how invariants and symmetry help, one domino example, common mistakes, and when to use them.
DFS — Depth-First Search Algorithm Explained
Depth-first search explained simply: what DFS does, how backtracking works, one clear example, and common DFS mistakes.
Dijkstra's Algorithm — Shortest Path Step by Step
Dijkstra's algorithm finds shortest paths in a weighted graph with nonnegative edges. Learn the idea, one example, and common mistakes.
Dynamic Programming — Memoization & Tabulation Explained
Learn what dynamic programming is, when it works, and how memoization and tabulation avoid repeated work.
Gradient Descent — Algorithm, Learning Rate & Variants
Gradient descent explained: what it is, how the learning rate works, and when batch, stochastic, and mini-batch methods are used.
Graph Coloring — Chromatic Number & Applications
Learn what graph coloring means, what the chromatic number measures, why an odd cycle needs three colors, and where coloring is used.
Hash Table — How Hashing Works & Collision Resolution
Learn what a hash table is, how hashing maps keys to array slots, why collisions happen, and how chaining and open addressing work.
K-Means Clustering — Algorithm & How It Works
What k-means clustering is, how the algorithm works, a simple example, common mistakes, and when k-means is a good fit.
Karnaugh Map — K-Map Simplification Guide
Learn Karnaugh maps fast: what a K-map is, how grouping works, one clear example, common mistakes, and when to use it.
Logistic Regression — Sigmoid Function & Classification
Learn what logistic regression is, how the sigmoid function turns a linear score into a probability, and how classification happens.
Machine Learning — Supervised, Unsupervised & Key Algorithms
Machine learning basics in plain language: what supervised and unsupervised learning mean, one worked example, and when common algorithms fit.
Neural Network — How Artificial Neural Networks Work
Learn what a neural network is, how layers turn inputs into predictions, one worked example, common mistakes, and how training changes the weights.
Object-Oriented Programming (OOP)
Object-oriented programming explained with classes, objects, one clear example, common mistakes, and when OOP is useful.
OSI Model — 7 Layers Explained
Understand the OSI model fast: what each of the 7 layers does, one clear example, and how the model helps with troubleshooting.
PCA — Principal Component Analysis Explained
Understand PCA fast: what principal component analysis does, when to center or scale, and one clear 2D example.
Process Scheduling — OS Concepts
Understand process scheduling in operating systems, compare FCFS, SJF, and round robin, and follow one clear worked example.
Recursion — How It Works, Examples & Base Cases
What is recursion in math? Learn how base cases and recursive steps work through a clear factorial example and common mistakes.
REST API — What It Is and How It Works
Learn what a REST API is, how requests and resources fit together, one clear example, and the mistakes that cause confusion.
RNN — Recurrent Neural Network & LSTM Explained
Learn what an RNN is, how hidden state carries sequence memory, why basic RNNs struggle on long inputs, and how LSTMs help.
Sorting Algorithms — Bubble, Merge, Quick Sort Compared
Learn what sorting algorithms do, how bubble sort, merge sort, and quick sort compare, and when each one makes sense.
SVM — Support Vector Machine Explained Simply
Understand what an SVM is, how margin and support vectors work, and when soft-margin or kernel SVMs are useful.
Transformer — Attention Mechanism & Architecture
Learn what transformer architecture is, how self-attention works, one worked example, common mistakes, and when transformers are used.
Truth Tables — AND, OR, NOT, XOR & Implications
Understand truth tables for AND, OR, NOT, XOR, and implication with one clear example, common mistakes, and when to use them.