MaThematics Behind Backpropagation | Theory and Python Code

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Mathematics Behind Backpropagation | Theory and Python Code
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 4h 37m | 1.2 GB
Instructor: Patrik Szepesi

Implement Backpropagation & Gradient Descent from scratch in your own neural network, then code it Without any Libraries

What you'll learn

• Understand and Implement Backpropagation by Hand and Code
  
• Understand the Mathematical Foundations of Neural Networks
  
• Build and Train Your Own Feedforward Neural Network in Python without any Libraries
  
• Explore Common Pitfalls in Backpropagation
  
• Numerically Calculate Derivatives, Partial Derivatives and Gradients through Examples
  
• Find the Derivatives of Loss Functions and Activation Functions
  
• Undestand What Derivatives are
  
• Visualize Gradient Descent in Action
  
• Implement Gradient Descent by Hand
  
• Use Python to code Multiple Neural Networks
  
• Undertand how Partial Derivatives Work in Backpropagation
  
• Understand Gradients and How they guide Machines to Learn
  
• Learn Why we Use Activation Functions
  
• Understand the Role of Learning Rates in Gradient Descent
  

Requirements

• basic python knowledge
  
• high school mathematics
  

Description

Unlock the secrets behind the algorithm that powers modern AI: backpropagation. This essential concept drives the learning process in neural networks, powering technologies like self-driving cars, large language models (LLMs), medical imaging breakthroughs, and much more.

In Mathematics Behind Backpropagation | Theory and Code, we take you on a journey from zero to mastery, exploring backpropagation through both theory and hands-on implementation. Starting with the fundamentals, you'll learn the mathematics behind backpropagation, including derivatives, partial derivatives, and gradients. We'll demystify gradient descent, showing you how machines optimize themselves to improve performance efficiently.

But this isn't just about theory-you'll roll up your sleeves and implement backpropagation from scratch, first calculating everything by hand to ensure you understand every step. Then, you'll move to Python coding, building your own neural network without relying on any libraries or pre-built tools. By the end, you'll know exactly how backpropagation works, from the math to the code and beyond.

Whether you're an aspiring machine learning engineer, a developer transitioning into AI, or a data scientist seeking deeper understanding, this course equips you with rare skills most professionals don't have. Master backpropagation, stand out in AI, and gain the confidence to build neural networks with foundational knowledge that sets you apart in this competitive field.

Who this course is for:

• Data Scientists who want to deepen their understanding of the mathematical underpinnings of neural networks.
  
• Aspiring Machine Learning Engineers who want to build a strong foundation in the algorithms that power AI.
  
• Software Developers looking to transition into the exciting world of machine learning and AI.
  
• Students and Enthusiasts eager to learn how machine learning really works under the hood.
  
• Professionals aiming to stay competitive in the era of LLMs and advanced AI by mastering skills beyond basic frameworks.
  

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