Introduction to Deep Learning

Just a heads up that HSE's Coursera Deep Learning course can be done entirely for free.

The Higher School for Economics in Russia has a Coursera course https://www.coursera.org/learn/intro-to-deep-learning/ has locked assignments if you don't subscribe BUT they put all the assignments on GitHub so you can do them offline. Good opportunity to try out ML on your own computer, with your own hardware.

Hello there! It seems our interests are quite similar :)

Though I haven't tried Sentiment Analysis and Recurrent Neural Networks, I tried just a bag of word and linear classifier for my tasks. For my classification purposes it was enough and accuracy was about ~96%.

It's still a low score that helps a lot, but requires a lot of improvements. Some of my ideas about it:

1. Bag of words doesn't take into account words order. So, instead, I need to analyze sequences. Introduction to Deep Learning (from HSE) on Coursera explains neural networks really well, AND it's including RNNs for analysing sequences (i.e. texts as well).
2. Once again, they explain about word2vec. So I'm planning to use fasttext embeddings.
3. To be honest, as a quick&dirty approach for my pet projects, I just analysed text manually. And this is exactly, that increased accuracy from ~70% to current 96% (as far, as I can remember):

So quick approaches are:

• Just a common sense: "if there are some words in a text", let's mark it, automatically, as 0. "If there are some other words" let's mark it as 1.
• otherwise, let's try to use several methods and vote (ensemble).

This is a really simple and dirty approach for classification tasks, though it works in my case.

Best of luck!

The classes I've complete are:

Machine Learning (https://www.coursera.org/learn/machine-learning/)

Introduction to Deep Learning (https://www.coursera.org/learn/intro-to-deep-learning/)

and I'm about half way through:

Bayesian Methods for Machine Learning (https://www.coursera.org/learn/bayesian-methods-in-machine-learning/)