Sonith ManakkatAlgorithms explained
A twelve-part, picture-led tour of how algorithms work, from judging speed with Big-O to sorting, searching, graphs, hashing, dynamic programming, machine learning and encryption.
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Each part pairs an everyday analogy and an animated picture with an expert layer. Read in order, or jump to the part you need.
Big-O: how we judge algorithms
What an algorithm is, why we count steps instead of seconds, and how Big-O describes the way work grows as a job gets bigger, with everyday pictures for each common class.
Searching: linear and binary search
Two ways to find an item in a list, shown as animations: look at everything, or halve a sorted list again and again. What each costs and when each is the right tool.
Sorting: bubble, insertion, merge and quick sort explained
Four ways to put things in order, shown as animated bars, with the idea behind each, what it costs, which are stable, and when each one is a good choice.
Hashing and hash tables
How a hash function turns a key into a bucket number so lookups take about one step on average, what collisions are, how tables grow, and why password hashing is a different job.
Graphs: BFS, DFS and shortest paths
How a map of towns and roads becomes a graph, and how breadth-first search, depth-first search and Dijkstra's algorithm explore it, with animated pictures and the costs of each.
Recursion and dynamic programming
How a problem that contains smaller copies of itself can be solved by recursion, why naive recursion can explode, and how remembering answers (dynamic programming) fixes it.
Greedy, divide and conquer, and backtracking
Three classic problem-solving styles, grab the best now, split the job, or try and back up, with everyday pictures, when each works, when it fails, and how to choose.
Trees and heaps
How trees organise data like a family tree, how a binary search tree finds things quickly, and how a heap always hands you the most urgent item first.
How ranking and recommendations work
A general, plain-language look at scoring, similarity, "people who liked this also liked that", the idea behind PageRank, and the cautions around bias, feedback loops and privacy.
How machine learning algorithms learn
Learning from examples instead of fixed rules, gradient descent as walking downhill, decision trees, neural networks, overfitting, and how this connects to language models.
Encryption and digital signatures, the basics
How locks and keys explain encryption, why there are two families (shared-key and public-key), what digital signatures prove, and how HTTPS combines them. Concepts only, no recipes.
Choosing the right algorithm
A decision cheat sheet that turns the whole series into questions you can ask about a problem, plus the practical advice experts give about measuring, clarity and using libraries.