Build a Simple Neural Network & Learn Backpropagation (Zerotomastery – Engsub)

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Build a Simple Neural Network & Learn Backpropagation – From Scratch in Python
Demystify the inner workings of modern AI by building your own neural network from the ground up—no frameworks, no shortcuts, just
pure Python and math
. This course is designed for
aspiring Machine Learning Engineers, Data Scientists, and AI Specialists
who want to truly understand how neural networks learn.
You’ll start with the
core mathematical foundations
, exploring the principles behind backpropagation and gradient descent. Then, step-by-step, you’ll translate these concepts into functional Python code, creating a fully working neural network without relying on pre-built machine learning libraries.
By the end of this course, you will:
Understand the
mathematics driving neural network training
Implement forward and backward passes entirely from scratch
Apply gradient descent to optimize model performance
Gain the confidence to tackle more advanced AI architectures

Whether you’re preparing for technical interviews, academic research, or simply want to go beyond the “black box” of machine learning, this course will give you the
practical coding experience and theoretical clarity
to master neural networks at their core.

Learn the fundamentals, code with precision, and truly understand how machines learn.
What you’ll learn:
Coding neural networks from scratch using only Python
 What backpropagation is and how it helps machines learn
 How to break down complicated math into simple, doable steps
 The easiest way to understand gradients and why they matter
 What’s really happening when a machine makes predictions
 How to train a smarter model by adjusting tiny details in code

Time Course:
39 Lectures + Documents

Instructor
: Patrik Szepesi
Total Weight:
636.64 MB
** Note
:  

Chú ý:

Link gốc:

https://zerotomastery.io/courses/neural-network-from-scratch/