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Prashant

26 Mar, 8 tweets, 2 min read

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 👇

Linear Regression
drive.google.com/file/d/1A1chyl…

Logistic Regression
drive.google.com/file/d/1AKE1Vj…

Unsupervised Learning
drive.google.com/file/d/1A2Ra60…

Neural Networks
drive.google.com/file/d/1AKl74N…

Anomaly and Recommender System
drive.google.com/file/d/1A74ZEn…

Advice for appyling ML
drive.google.com/file/d/1A3p9QQ…

I'll put out some other notes as well soon.
Hope it helps!

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Prashant

@capeandcode

24 Mar

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.

Read 9 tweets

Prashant

@capeandcode

11 Feb

Ever heard of Autoencoders?

The first time I saw a Neural Network with more output neurons than in the hidden layers, I couldn't figure how it would work?!

#DeepLearning #MachineLearning
Here's a little something about them: 🧵👇

Autoencoders are unsupervised neural networks whose architecture you can picture as two funnels connect from the narrow ends.

These networks are primary focus for compression tasks of data in Machine Learning.

We feed them the data so that they can learn the most important features, a smaller representation while keep the integrity of the data.

Later when someone needs, can just take that small representation and recreate the original, just like a zip file.📥

Read 10 tweets

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