Introduction to Deep Learning

share ›
‹ links

Below are the top discussions from Reddit that mention this online Coursera course from National Research University Higher School of Economics.

The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding.

Recurrent Neural Network Tensorflow Convolutional Neural Network Deep Learning

Reddsera may receive an affiliate commission if you enroll in a paid course after using these buttons to visit Coursera. Thank you for using these buttons to support Reddsera.

Taught by
Evgeny Sokolov
Senior Lecturer
and 4 more instructors

Offered by
National Research University Higher School of Economics

Reddit Posts and Comments

0 posts • 4 mentions • top 4 shown below

r/learnmachinelearning • post
76 points • Fear_UnOwn
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 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.

r/learnmachinelearning • comment
1 points • egslava

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!

r/computervision • comment
7 points • lifeadvicesponge

I can refer you to some MOOCs for deep learning:

  • Check out Stanford's CS231n course . It's specifically about applications of deep learning to computer vision. Have a look at both the Spring 2016 and Winter 2017 iterations.

  • Stanford's CS224n course covers Deep Learning applied to natural language processing.

  • Apart from that you could refer to Andrew Ng's Deep Learning specialisation on Coursera. The fourth course covers CNNs (including things like YOLO and Siamese Networks which aren't covered in CS231n; at least the spring 2016 version). The fifth course covers sequence models (RNNs, LSTM, GRU)

  • You can also look at Coursera's Intro To Deep Learning by the Russian University HSE. While the quality of lectures is a hit and miss their programming assignments are more comprehensive than the Andrew Ng courses. It also covers autoencoders which aren't covered in any other course mentioned above.

r/fantasybaseball • comment
2 points • RichardMuncherIII

The classes I've complete are:

Machine Learning (

Introduction to Deep Learning (

and I'm about half way through:

Bayesian Methods for Machine Learning (