deep learning

Object Detection with Deep Learning

One of the most interesting topics in the Coursera Deep Learning specialization is the “YOLO” algorithm for object detection. I often find it helpful to describe algorithms in my own words to solidify my understanding, and that is precisely what I will do here.

Thoughts on the Coursera Deep Learning Specialization

I recently completed the Deep Learning specialization on Coursera from Over five courses, they go over generic neural networks, regularization, convolutional neural nets, and recurrent neural nets. Having completed it, I would say the specialization is a great overview, and a jumping off point for learning more about particular techniques.

Distribution of Local Minima in Deep Neural Networks

The “unreasonable effectiveness of deep learning” has been much discussed. Namely, as the cost function is non-convex, any optimization procedure will in general find a local, non-global, minimum. Actually, algorithms like gradient descent will terminate (perhaps because of early stopping) before even reaching a local minimum.

Computer Vision Cheat Sheet

I am currently working through Convolutional Neural Networks, the fourth course in the Coursera specialization on Deep Learning. The first week of that course contains some hard-to-remember equations about filter sizes and padding and striding and I thought it would be helpful for me to write it out for future reference.

Deep Learning Checklist

Recently I started the Deep Learning Specialization on Coursera. While I studied neural networks in my masters program (from Andrew Ng himself!), that was a long time ago and the field has changed considerably since then.