Lecture 27 -Coding the ADAM optimizer for neural networks

In this lecture, we learn about the most practical optimizer used in many modern ML frameworks: ADAM optimizer.

We train a neural network using this optimizer, in Python.

Everything is explained on a whiteboard, and then in Python code.

0:00 Gradient descent, Momentum and RMSProp
14:38 ADAM concept
18:48 ADAM mathematics
22:40 Neural network architecture
24:43 Python Code implementation
41:03 Visual results through video

Jupyter Notebook to access full code: https://drive.google.com/file/d/1LrRd...

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As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We  will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there.

Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch.

No cost. No hidden charges.  Pure old school teaching and learning.

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