Views<\/p>\n
Build a Simple Neural Network & Learn Backpropagation \u2013 From Scratch in Python
Demystify the inner workings of modern AI by building your own neural network from the ground up\u2014no 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\u2019ll start with the
core mathematical foundations
, exploring the principles behind backpropagation and gradient descent. Then, step-by-step, you\u2019ll 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<\/p>\n
Whether you\u2019re preparing for technical interviews, academic research, or simply want to go beyond the \u201cblack box\u201d of machine learning, this course will give you the
practical coding experience and theoretical clarity
to master neural networks at their core.<\/p>\n
Learn the fundamentals, code with precision, and truly understand how machines learn.
What you’ll learn:
Coding neural networks from scratch using only Python
\u00a0What backpropagation is and how it helps machines learn
\u00a0How to break down complicated math into simple, doable steps
\u00a0The easiest way to understand gradients and why they matter
\u00a0What\u2019s really happening when a machine makes predictions
\u00a0How to train a smarter model by adjusting tiny details in code<\/p>\n
Time Course:
39\u00a0Lectures + Documents<\/p>\n
Instructor
: Patrik Szepesi
Total Weight:
636.64\u00a0MB
** Note
: \u00a0<\/p>\n
Ch\u00fa \u00fd:<\/p>\n
Link g\u1ed1c:<\/p>\n
https:\/\/zerotomastery.io\/courses\/neural-network-from-scratch\/<\/p>\n
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