Gradient Descent Continued | Deep Learning basics

Hello, everyone! 👋 Welcome back to our channel! In this video, we continue to dive deeper into the concept of Gradient Descent. 🌟
Gradient Descent Part 1 - • Gradient Descent | Dee...
🔎 What We'll Cover:
🏞️ Convex Loss Function:
We'll start by understanding a convex loss function. Imagine a smooth, parabola-shaped curve with a single minimum point. 🌐
The derivative at any point on this curve points in the direction of the steepest ascent (going up). 📈 But we want to go down to reach the lowest point (minimum loss). 📉
🎯 Goal of Gradient Descent:
Our goal is to minimize the loss by descending the curve. To do this, we need to adjust the weights and biases. ⚖️
🧩 General Equation:
We'll introduce the general equation of gradient descent:
θ_new = θ_old - learning_rate * gradient
Where:
θ (theta): Parameters (weights and biases)
learning_rate: Controls how big a step we take
gradient: Direction of steepest ascent
🔍 Breaking Down the Equation:
θ_old: The current value of the parameter.
learning_rate: If it's too low, learning is super slow 🐢; if it's too high, we might miss the minimum and have to adjust multiple times 🏃‍♂️.
gradient: Tells us the direction and rate of change.
🛤️ Opportunities and Challenges:
Using a 3D loss function, we'll show how gradient descent behaves in different regions:
Flat regions: Gradient descent runs slowly, making it hard to find the optimal solution 😓.
Steep regions: Weights and biases get updated much faster 🚀.
🚀 Beyond Basic Gradient Descent:
We'll hint at advanced algorithms that can overcome the challenges of basic gradient descent. Stay tuned for more on this in upcoming videos! 🎉
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