Startup Data Science

Alex gives a quick recap of Lesson 5, using embeddings with imdb review data to categorize movies into clusters using Natural Language Processing (NLP). Edderic, Apurva, and Alex discuss what they're excited about with using NLP and also speak to their motivation as they continue to learn deep learning.

Alex is excited about collaborative filtering and he could see using it in his startup to help people unlearn toxic behaviors and beliefs in a productive way. Apurva started working remotely; she found it hard to stay motivated to study. She has issues with collaborative filtering in Netflix; she feels like Netflix's recommendation algorithm is not good for discovering new things because she thinks the recommendations tend to be similar to the past. Edderic's been busy with work at Lingo Live. Edderic enjoys the part of the video lesson where Jeremy destroys the movie data set recommender benchmark seamlessly with a Neural Network.

Apurva loved Jeremy's presentation using Excel to show how calculations are being made; it was a great confidence-building exercise for her to replicate it in Excel. Edderic's excited about Jeremy's claim that Convolutional Neural Networks are doing well in Speech Recognition. There are tons of machine learning algorithms out there; he thinks it would be nice to have just one super algorithm/architecture to rule them all. Alex explains his idea of convolution through an analogy.

Alex thinks dropout is cool. He's still not quite sure what batch normalization is. Regarding ImageNet competition, Apurva, along with offering tips to staying motivated to learning says that instead of creating "new" models, people are only doing ensembling now to get a marginal edge over everyone else. Edderic announces revamping his PC workstation for deep learning (bye-bye Amazon!)

Alex promises to do 20 min. of Data Science every day to keep making progress. Edderic learns that Apurva hasn't submitted the Cats and Dogs Kaggle submission yet, so he feels a little bit better about himself for not submitting yet either. Alex mistakes Natural Language Processing for Neuro-Linguistic Programming (whoops!)

Apurva feels impressed because for complex tasks like image classification, "simple" things like linear regression is sometimes good enough. Alex and Edderic warn listeners to basically allocate more time to this lesson, because there's a lot of hard reading. Edderic confesses that he failed to implement the backpropagation algorithm in the machine learning course he took during college, and promises to himself that he would get salvation by implementing it correctly this time. Image is taken from Michael Nielsen's Neural Networks and Deep Learning book (http://neuralnetworksanddeeplearning.com/chap2.html)

Alex Au, Apurva Naik, and Edderic Ugaddan discuss the first part of Lesson 2 of Practical Deep Learning for Coders. Apurva and Alex talks about feeling that the segue-way between lesson 1 and 2 was not smooth. For lesson 1, Jeremy Howard, one of the instructors of Practical Deep Learning for Coders course, asked people to do a submission to Kaggle without leaving any hint on how to do it. Apurva feels "betrayed" that Jeremy did not point out that he would talk about it in Lesson 2. As a result of not having seen the next episode for a while, she had too many unfruitful attempts. Edderic suggests that the course should be "Practical Deep Learning for Computer Scientists" instead of "Practical Deep Learning for Coders" due to lots of mathematical foundation work (Calculus, Linear Algebra, etc.) needed for really understanding the theory. Alex asks about the difference between the "test" folder and the "valid" folder.

Alex Au, Apurva Naik, and Edderic Ugaddan discuss what it's like to go through the setting up for AWS instance so that they could run the 7 lines of code that abstracted the VGG model, which had best-in-class performance a few years back in classifying cats and dogs. Credit for the cat and dog podcast image goes to kitty.green66 (https://www.flickr.com/photos/53887959@N07/4985437148/)

Apurva Naik, Alex Au, and Edderic Ugaddan discuss the first half of the Lesson 1 of Practical Deep Learning for Coders (http://course.fast.ai/lessons/lesson1.html). We discuss what we find difficult, what we are excited about, with regards to the lesson material.

In Episode 0, Alex Au, Apurva Nalik, and Edderic Ugaddan introduce themselves, talk about their background, why they wanted to get into Data Science, and what the format of the podcast is generally going to be.