Introduction to Neural Networks (Lecture 4) скачать в хорошем качестве

Introduction to Neural Networks (Lecture 4)

Welcome to the fourth lecture of my Deep Learning series! 📉 In this video, we take the fundamental concept of differentiation we learned previously and apply it to a multivariate mathematical expression: d = a * b +. c This is the first step toward understanding how Neural Networks actually learn via **Backpropagation**. We start by closing the loop on the previous lecture, analyzing the condition where the gradient is zero and what that implies for the function. Then, we move to the core of this lecture: dissecting a computational graph. We perform a "Forward Pass" to calculate the output, and then intuitively perform a "Backward Pass" using calculus to find the derivatives of the output with respect to the inputs ($a, b, c$). Finally, we prove our mathematical derivation is correct by writing Python code to calculate the slopes numerically, demonstrating how we can manipulate the inputs to minimize the output—just like minimizing Loss in a Neural Network. In this video, we cover: ✅ *Gradient Analysis:* What happens when the slope is zero (stationary points). ✅ *Multivariate Calculus:* Breaking down the expression ✅ *Forward Pass:* Calculating the result of an expression from inputs to output. ✅ *Manual Backpropagation:* Deriving gradients analytically using the Chain Rule (intuition phase). ✅ *Python Verification:* Using the limit definition of a derivative (f(x+h)) in code to verify our calculus. ✅ *Loss Minimization:* demonstrating how to nudge inputs (a, b, c) based on their gradients to decrease the final output. Resources: 🔗 GitHub Repository (Code & Notes): https://github.com/gautamgoel962/Yout... 🔗 Follow me on Instagram:   / gautamgoel978   Prerequisites: Previous Lecture (Understanding Differentiation) Basic Python Programming High School Math (Basic Algebra & derivative rules) Subscribe to continue the journey of building Micrograd and LLMs from scratch. Next up, we start building the `Value` class! 🧠💻 #DeepLearning #Backpropagation #Calculus #GradientDescent #NeuralNetworks #Python #Hindi #MathForAI #Micrograd