Understand how artificial neural networks work from the ground up — starting with the mathematics and building toward implementing your own neural network.
Artificial Neural Networks can seem like a black box filled with complicated mathematics, layers, and formulas.
This course takes the opposite approach.
We start from the beginning and build our understanding step by step — learning the mathematics we actually need, understanding what each concept means, and connecting it directly to how a neural network learns.
Instead of simply using a deep-learning framework, the goal is to understand what is happening underneath it.
The journey begins with the mathematical foundations required for neural networks, including concepts such as functions, derivatives, and other essential mathematics. Difficult concepts are explored in depth rather than rushed through.
From there, we progressively move toward understanding:
The objective is not just to make a neural network work.
The objective is to understand why it works.
This course is part of the broader MADE FOR NOTHING — Learning AI journey: learning by reading, experimenting, researching, building, and explaining.
Beginners who want to understand neural networks from the ground up,
Students beginning their AI/ML journey,
Developers who can use AI libraries but want to understand what happens underneath,
Learners who want to understand the mathematics behind neural networks,
Anyone who wants to build an ANN from first principles.
Structured sequential lectures with dedicated video, proofs, and verification checkpoints.
Build the basic mathematical foundation needed to understand neural networks. This lesson introduces the essential ideas of variables, constants, expressions, equations, functions, graphs, and mathematical notation that we will use throughout the course.
Build an intuitive and mathematical understanding of derivatives. Learn what a derivative actually represents, how it measures the rate of change of a function, how slopes and tangent lines connect to derivatives, and why derivatives become essential for understanding how neural networks learn.
I'm learning AI by starting from the fundamentals rather than treating modern AI systems as black boxes.
Through MADE FOR NOTHING, I research concepts, experiment with them, build things, and document what I learn.
The approach is simple:
Read → Experiment → Research → Build → Explain
This course follows the same philosophy.
Rather than presenting neural networks as a collection of formulas to memorize, we work toward understanding what each part does, why it exists, and how the pieces come together.
Instructor Note
This course is being built as an ongoing learning journey.
The mathematics is not separated from the neural network concepts. We learn each mathematical idea when it becomes useful for understanding what the network is doing.
For example, derivatives are explored deeply because they become important when we eventually need to understand how a neural network learns and adjusts its parameters.
The goal throughout the course is understanding before abstraction.
Immediate lifetime access to all 2 lecture units, marginalia proofs, and conceptual checkpoints. Zero third-party trackers.