Artificial Neuron Explained: From Linear Regression to Logistic Regression

Описание: How do neural networks actually learn? In this video, we build intuition for neural networks from the ground up—starting from a simple artificial neuron and scaling up to modern architectures used in computer vision and AI.

Chapters:
0:00 Welcome
0:33 Let's Make a Neuron
2:37 Neural Network (MLP)
5:15 Training-Validation-Test Set
7:40 Cross-Validation
9:37 Backpropagation
17:48 Neural Networks in Self-Driving
19:42 Autograd and Gradient Descent
24:10 Stochastic Gradient Descent
25:10 Overview of Training
31:52 Convolution Neural Networks (CNNs)
34:48 Activation Functions
35:25 Hyperparameters
36:26 What do networks see?
37:33 Regression
38:20 Regression Examples
41:56 Understanding Check

We’ll cover:
✅ What a neural network really is and how artificial neurons work
✅ How networks are trained using gradient descent
✅ The intuition behind backpropagation and how errors flow backward through the network
✅ The difference between regression and classification tasks
✅ Why Convolutional Neural Networks (CNNs) are powerful for images
✅ Practical examples showing how neural networks solve real-world problems

Rather than just presenting formulas, this video focuses on building intuition—connecting the math, geometry, and learning process behind neural networks. By the end, you’ll understand how models adjust millions of parameters to learn patterns from data.

This lecture is part of Embodied AI Demystified with Prof. Nitin, where we break down core ideas behind perception, robotics, and AI—from classical methods to modern deep learning.

Whether you're new to robotics or an AI enthusiast, this video will give you a clear and fun introduction to the world of robots!

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