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

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

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