Introduction to Neural Networks (Lecture 15) скачать в хорошем качестве

Introduction to Neural Networks (Lecture 15)

#AI #Python #DeepLearning #Micrograd #Coding #NeuralNetworks Welcome to the fifteenth lecture of my Deep Learning series! 🧠💻 We have studied the math, visualized the curves, and understood the "why" behind activation functions. Now, it’s time to get our hands dirty. In this video, we open up our code editor and implement Sigmoid, Tanh, and ReLU directly into our Value class in the Micrograd library. This isn't just a copy-paste coding session. We encounter a critical engineering decision: Design Choices. Should we build Tanh using the primitive operations we already made (addition, division, powers)? Or should we implement it as its own atomic operation? In this lecture, we compare both approaches and discover why one is significantly better for the efficiency of our future Backpropagation engine. In this video, we cover: ✅ Python's Math Library: We introduce the math module to handle exponential calculations (e^x) required for our S-curves. ✅ Implementing Sigmoid: We code the formula. We analyze two ways to do this: building a long chain of operations vs. creating a single function, and why the latter simplifies our derivative calculation later. ✅ The Tanh "Design Choice": We attempt to build Tanh using its raw formula involving exponentials and division. We realize this creates a massive computational graph with 7-8 intermediate gradients to calculate. ✅ Optimization & Efficiency: We discuss why defining Tanh and Sigmoid as "atomic" operations is a "Good Design Choice." This reduces the computational overhead during the backward pass by using the clean analytical derivatives we derived in previous lectures (e.g., ✅ Implementing ReLU: We code the Rectified Linear Unit. We look at how to handle the logic: if the value is greater than 0, pass it through; otherwise, return 0. We verify that our implementation correctly handles the "dead neuron" state. Resources: 🔗 GitHub Repository (Code & Notes): https://github.com/gautamgoel962/Yout... 🔗 Follow me on Instagram:   / gautamgoel978   Subscribe and code along! We have now built the forward pass for our neurons. In the next video, we tackle the most magical part of Neural Networks: Backpropagation. We will manually implement the backward pass to teach our network how to learn! 📉🔥 #deeplearning #Python #Micrograd #ReLU #Sigmoid #Tanh #SoftwareEngineering #DataScience #MachineLearning #Hindi #AI #Backpropagation #ComputationalGraph