Here are the links for all the notes that I have from the Andrew NG Machine Learning Course that I made back in 2016
This was my first exposure to #MachineLearning They helped me a lot and I hope anyone who's just starting out and prefers handwritten notes can reference these 👇
Gradient Descent is great but there are a whole bunch of problems associated with it.
Getting stuck in the local minima while browsing the solution space is one of the major issues.
A possible Solution?
SIMULATED ANNEALING
Here's a little something about it 🧵👇
The method of Simulated Annealing in Optimization is analogical to the process of Annealing in Metallurgy ⚗️🔥, hence the name.
We get stuck in the local minima because we tend to always accept a solution that seems best in shortsight. We just move in the downwards direction ⬇️ (negative gradient) and not upwards⬆️
So once we reach a point which is low but not the lowest, we may end up getting stuck.