Introduction to Neural Networks (Lecture 3) скачать в хорошем качестве

Introduction to Neural Networks (Lecture 3)

Here is the description for the current video, following the format you provided: Welcome to the third lecture of my Deep Learning series! 📉 In this video, we pause the coding of complex networks to master the mathematical engine that powers them: Differentiation. Before we build our own framework, it is crucial to intuitively understand what a derivative is, beyond just the formulas we learned in school. We break down the physical significance of differentiation, treating it as the sensitivity of a function to its input. We visualize this using simple parabolic functions and explore how a tiny change in input affects the output. We then translate this mathematical definition—the limit definition of a derivative—directly into Python code to calculate slopes numerically. Finally, we connect the dots to Neural Networks, explaining how the "sign" of the slope (positive or negative) dictates exactly how we should adjust our weights to minimize the Loss function. In this video, we cover: ✅ Physical Significance: Understanding derivatives as "Rate of Change" and "Slope." ✅ Visual Intuition: Using graphs and geometry to see how functions grow or shrink. ✅ The Math: Deriving the formula ​ and understanding limits. ✅ Python Implementation: Writing a script to calculate the numerical derivative of a function from scratch. ✅ The "Why" for AI: How gradients guide us in minimizing Loss (The relationship between Slope and Weight updates). ✅ Analytical vs. Numerical: Comparing the calculus formula results with our Python estimation. Resources: 🔗 GitHub Repository (Code & Notes): https://github.com/gautamgoel962/Yout... 🔗 Follow me on Instagram:   / gautamgoel978   Prerequisites: Basic Python Programming High School Math (Functions & Basic Algebra) A desire to understand the math behind the magic! Subscribe to continue the journey of building Micrograd and LLMs from scratch. Let's conquer the math together! 🧠💻 #DeepLearning #Differentiation #Calculus #GradientDescent #NeuralNetworks #Python #Hindi #MathForAI #MachineLearning