Links

I have been meaning to share the links to some of the pages that I follow for learning Neural Networks, Deep Learning, Motion planning algorithms, Q-Learning using function approximation and Deep Reinforcement Learning. Most of these pages are good if you want to code from scratch. 

This is an online book on Neural Networks and Deep Learning that I am following right now. The codes are all in Python. The exercises are thoughtful and the explanation is quite easy to follow. Here's the link: Michael Nielsen's online book on Neural Network and Deep Learning

A great site for graph search algorithms like BFS, BFS, Dijkstra's and A* and its variants (gaming applications): Amit's A* Pages

David Silver's course on Reinforcement Learning

News

October 19 - 21, 2016 : I just attended Grace Hopper Celebration in Houston. I have heard from many former attendees that this conference boosts your spirits and resets your goals if you are distracted. I agree. It is a "celebration" in its true sense. You do feel celebrated as a woman, starting from the keynotes by some highly successful women in tech to meeting representatives from different companies. But what touched me the most is to meet a "galaxy" of women in computing who come from a plethora of backgrounds, like some of the most accomplished graduate students to mothers who decided to pursue computer science education late in their life. There were 16000 of us. No matter how much of an outlier that you think you are, you are sure to find someone who had very similar life experiences as you. I was thrilled to meet an engineer from Google who started out her career in arts and architecture, very much like I did. As for the technical talks, I loved the workshop on design of self-driving cars, offered by four Google engineers. They demonstrated how a mechanical engineer, a systems engineer, a user experience product manager and a software engineer would collaboratively solve a technical challenge in a corporate environment. Overall GHC exceeded my expectations.