Visual, textbook-style volumes across every subject we teach. Each one builds from first principles up to real, practical depth — starting with our machine learning track.
The big picture before the equations
A gentle, big-picture course for absolute beginners — starting with why AI exists at all, and building up a clear, correct mental model of how Artificial Intelligence, Machine Learning, and Deep Learning actually relate to each other.
From a single neuron to a network that learns
A story-driven, from-first-principles walk through neural networks — starting with a single artificial neuron and ending with a fully trained multi-layer perceptron, backpropagation, optimizers, and everything in between.
From pixels to predictions — how machines learn to see
A story-driven, from-first-principles walk through convolutional neural networks — starting with why plain MLPs fail on images and ending with modern architectures, training pipelines, and the interview questions that test it all.
How machines learn to remember — and forget — on purpose
A story-driven, from-first-principles walk through Long Short-Term Memory networks — starting with why sequences defeat ordinary neural networks and ending with complete, real-world projects, advanced architectures, and the interview questions that test it all.
The art and science of making models that actually work
A rigorous, story-driven journey through hyperparameter optimization — from understanding what hyperparameters are and why they matter, through the mathematics of search, all the way to AutoML, experiment tracking, and production best practices.
Learning to reason clearly about uncertainty
A story-driven, from-first-principles walk through probability theory — how to quantify uncertainty, update beliefs with evidence, and reason correctly about chance in data, models, and the real world.
Modular arithmetic, linear congruences, queueing theory, and optimization
Course notes and previous year questions for Mathematical Analysis for Computer Science (CSE-3102), 3rd Year 1st Semester of the JnU B.Sc. in CSE curriculum — covering modular arithmetic, linear congruences, queueing theory (M/M/1, M/M/∞), gradient descent, differential calculus, and constrained optimization (Lagrange multipliers).
Teaching models to learn without giving away secrets
A ground-up journey through privacy in machine learning — from understanding why trained models leak information, to threat models and privacy attacks, to the mathematical guarantees of differential privacy and how to build systems that are both useful and safe.
Rasterization, 3D transformations, viewing pipelines, lighting, rendering, and computer animation
Course notes and learning materials for Computer Graphics and Animation (CSE-4105), 4th Year 1st Semester of the JnU B.Sc. in CSE curriculum — covering rasterization algorithms, 2D/3D transformations, viewing & projection, illumination models, rendering, and animation techniques.
Intelligent agents, search, knowledge representation, reasoning, and learning
Course notes and learning materials for Artificial Intelligence (CSE-4101), 4th Year 1st Semester of the JnU B.Sc. in CSE curriculum — covering intelligent agents, problem solving by search, knowledge representation and reasoning, uncertain reasoning, machine learning foundations, and AI applications.
Securing data and networks, from classical ciphers to modern protocols
Course notes and learning materials for Cryptography and Network Security (CSE-4109), 4th Year 1st Semester of the JnU B.Sc. in CSE curriculum — covering security goals, classical ciphers, symmetric and asymmetric cryptography, hash functions, digital signatures, and real-world network security protocols.