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Understanding optimizers is essential if you want to truly master deep learning.

In this video, we break down the core optimization algorithms used to train neural networks and transformer models: Gradient Descent (GD), Stochastic Gradient Descent (SGD), Momentum, RMSprop, and Adam.

Before we dive into Adam and AdamW in the next video, this episode gives you the complete foundation you need to understand how models actually learn.

You’ll learn:

• Why optimization is necessary in deep learning
• How gradient descent works mathematically
• The difference between GD and SGD
• Why Momentum helps accelerate convergence
• How RMSprop adapts learning rates
• Why Adam became the most widely used optimizer
• How optimizers relate to vanishing gradients and unstable training

If you’re building transformers, training LLMs, or learning machine learning from scratch, this video is a must-watch.

This is part of our deep learning series on Build AI with Sandeep, where we break down complex AI concepts into clear, practical explanations.

Next video: We go deep into Adam and AdamW — the real fix.
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