Adit Deshpande

Adit Deshpande

Student at University of California, Los Angeles

Bio: I'm a second year undergraduate student currently studying at UCLA. I'm majoring in computer science while also pursuing a minor in Bioinformatics. I've had two research internships in my career, one at Boston University and one at the U.S Naval Research Laboratory in Washington D.C.. I'm passionate about applying my knowledge of computer science and machine learning to areas in healthcare where we can really engineer better solutions for helping doctors and taking care of patients.

How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)

How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sort...

Introduction Chatbots are “computer programs which conduct conversation through auditory or textual methods”. Apple’s Siri, Microsoft’s Cortana, Google Assistant, and Amazon’s Alexa are four of the most popular conversational agents today. They can help you get directions, check the scores of sports games, call people in your address book, and can accidently make you order a $170 dollhouse. These products all […]

The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

The 9 Deep Learning Papers You Need To Know About (Understanding ...

Introduction Link to Part 1 Link to Part 2 In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why […]

Deep Learning Research Review Week 2: Reinforcement Learning

Deep Learning Research Review Week 2: Reinforcement Learning

This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning. Last time was Generative Adversarial Networks ICYMI Introduction to Reinforcement Learning 3 Categories of Machine Learning Before […]

Deep Learning Research Review Week 1: Generative Adversarial Nets

Deep Learning Research Review Week 1: Generative Adversarial Nets

This week, I’ll be doing a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week I’ll begin with Generative Adversarial Networks.  Introduction According to Yann LeCun, “adversarial training is the coolest thing since sliced bread”. I’m inclined […]

Analyzing The Papers Behind Facebook’s Computer Vision Approach

Analyzing The Papers Behind Facebook’s Computer Vision Appr...

Introduction You know that company called Facebook? Yeah, the one that has 1.6 billion people hooked on their website. Take all of the happy birthday posts, embarrassing pictures of you as a little kid, that one family relative that likes every single one of your statuses, and you have a whole lot of data to […]

A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

A Beginner’s Guide To Understanding Convolutional Neural Ne...

Introduction Link to Part 1 In this post, we’ll go into a lot more of the specifics of ConvNets. Disclaimer: Now, I do realize that some of these topics are quite complex and could be made in whole posts by themselves. In an effort to remain concise yet retain comprehensiveness, I will provide links to […]

A Beginner’s Guide To Understanding Convolutional Neural Networks

A Beginner’s Guide To Understanding Convolutional Neural Ne...

Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that […]