Udemy – Mathematics Behind Backpropagation | Theory and Python Code 2025-1

Udemy – Mathematics Behind Backpropagation | Theory and Python Code 2025-1 Downloadly IRSpace

Aug 16, 2025 - 06:54 Updated: Aug 17, 2025 - 19:29

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Udemy – Mathematics Behind Backpropagation | Theory and Python Code 2025-1

The Mathematics Behind Backpropagation | Theory and Python Code course explores the secrets of the algorithm that powers modern artificial intelligence: backpropagation. This fundamental concept drives the learning process in neural networks and enables technologies like self-driving cars, large language models, advances in medical imaging, and much more. This course takes students on a journey from beginner to mastery, exploring backpropagation from both theory and practical implementation. Starting with the basics, the course teaches the mathematics behind backpropagation, including derivatives, partial derivatives, and gradients. It also explains the concept of gradient descent in simple terms and shows how to efficiently optimize machine performance.

But this course is not just about theory; participants will dive into the practical side of things and implement post-propagation from scratch. At first, all calculations are done manually to gain a deep understanding of each step. Then, they will move on to Python programming and build their own neural network without using any pre-built libraries or tools. By the end of this course, graduates will have a complete understanding of how post-propagation works, from the math to the code and beyond. Whether you are an aspiring machine learning engineer, a software developer entering the world of AI, or a data scientist looking for a deeper understanding, this course will equip you with rare skills that most professionals lack. Stand out in the AI field by mastering post-propagation and gain the confidence to build neural networks with the foundational knowledge that will set you apart in this competitive arena.

What you will learn

Understanding and implementing manual and post-release coding
Understanding the mathematical foundations of neural networks
Build and train your own feedforward neural network in Python without using any libraries
Review of common problems in post-release
Numerical calculation of derivatives, partial derivatives, and gradients through examples
Finding derivatives of loss functions and activation functions
Understanding the concept of derivatives
Visually observe the gradient descent function
Manual implementation of gradient descent
Using Python to code multiple neural networks
Understanding how partial derivatives work in post-diffusion
Understanding gradients and how they guide machines to learn
Learn why activation functions are used
Understanding the role of learning rate in gradient descent

This course is suitable for people who:

Data scientists who want to gain a deeper understanding of the mathematical foundations of neural networks.
Passionate machine learning engineers who want to build a strong foundation in the algorithms that power AI.
Software developers looking to enter the exciting world of machine learning and artificial intelligence.
Students and enthusiasts who are eager to learn how machine learning actually works behind the scenes.
Professionals who strive to remain competitive in the era of large language models and advanced artificial intelligence by mastering skills beyond basic frameworks.