October 2017

Ambiguity

Ambiguity

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One of the problems with teaching computers to understand natural language, is that much of the meaning in what people say is actually hidden in what they don’t say. As humans, we trivially interpret the meaning of ambiguous words, written or spoken, according to their context. For example, this blog post is published in a blog that largely discusses natural language processing, so if I write “NLP”, you’d know I refer to natural language processing rather than to neuro-linguistic programming. If I […]

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Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS

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I’ve never worked in the field of computer vision and has no idea how the magic could work when an autonomous car is configured to tell apart a stop sign from a pedestrian in a red hat. To motivate myself to look into the maths behind object recognition and detection algorithms, I’m writing a few posts on this topic “Object Detection for Dummies”. This post, part 1, starts with super rudimentary concepts in image processing and a few […]

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Weekly Review: 10/28/2017

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This was a pretty busy week with a lot going on, but I finally seem to be settling into my new role! The study for Aerial Robotics is almost over with a week to go. There hasn’t been much coding in this course, but that was to be expected since it was more about PID-Control Theory and quadrotor dynamics. I am particularly interested in the Capstone/’final’ project for this course, which would involve building an autonomous robot in […]

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Weekly Review: 10/21/2017

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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. […]

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Learning Word Embedding

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Human vocabulary comes in free text. In order to make a machine learning model understand and process the natural language, we need to transform the free-text words into numeric values. One of the simplest transformation approaches is to do a one-hot encoding in which each distinct word stands for one dimension of the resulting vector and a binary value indicates whether the word presents (1) or not (0).

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