# Weekly Review: 10/21/2017

Its been a long while since I last posted, but for good reason! I was busy shifting base from Google’s Hyderabad office to their new location in Sunnyvale. This is my first time in the USA, so there is a lot to take in and process!

Anyway, I am now working on Google’s Social-Search and Ranking team. At the same time, I am also doing Coursera’s Robotics Specialization to learn a subject I have never really touched upon. Be warned if you ever decide to give it a try: their very first course, titled Aerial Robotics, has a lot of linear math and physics involved. Since I last did all this in my freshman year of college, I am just about getting the weeks done!

Since I already have my plate full with a lot of ToDos, but I also feel bad for not posting, I found a middle ground: I will try, to the best of my ability, to post one article each weekend about all the random/new interesting articles I read over the course of the week. This is partly for my own reference later on, since I have found myself going back to my posts quite a few times to revisit a concept I wrote on. So here goes:

Eigenvectors & Eigenvalues

Anything ‘eigen’ has confused me for a while now, mainly because I never understood the intuition behind the concept. The highest-rated answer to this Math-Stackexchange question did the job: Every square matrix is a linear transformation. The corresponding eigenvectors roughly describe how the transformation orients the results (or the directions of maximum change), while the corresponding eigenvalues describe the distortion caused in those directions.

Transfer Learning

Machine Learning currently specializes in utilizing data from a certain {Task, Domain} combo (for e.g., Task: Recognize dogs in photos, Domain: Photos of dogs) to learn a function. However, when this same function/model is used on a different but related task (Recognize foxes in photos) or a different domain (Photos of dogs taken during the night), it performs poorly. This article discusses Transfer Learning, a method to apply knowledge learned in one setting on problems in different ones.

Dynamic Filters

The filters used in Convolutional Neural Network layers usually have fixed weights at a certain layer, for a given feature map. This paper from the NIPS conference discusses the idea of layers that change their filter weights depending on the input. The intuition is this: Even though a filter is trained to look for a specialized feature within a given image, the orientation/shape/size of the feature might change with the image itself. This is especially true while analysing data such as moving objects within videos. A dynamic filter will then be able to adapt to the incoming data, and efficiently recognise the intended features inspite of distortions.