Practical Deep Learning For Coders

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step one—learning how to get a GPU server online suitable for deep learning—and go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems. There are around 20 hours of lessons, and you should plan to spend around 10 hours a week for 7 weeks to complete the material. The course is based on lessons recorded during the first certificate course at The Data Institute at USF. Part 2 will be taught at the Data Institute from Feb 27, 2017, and will be available online around May 2017.

1. Recognizing cats and dogs
2. Convolutional neural networks
3. Why deep learning. Intro to convolutions
4. CNN architecture basics. Avoiding over and under-fitting
5. CNN/SGD in Excel. Pseudo-labeling. Collaborative filtering
6. Intro to NLP, keras functional API, and RNNs
7. Embeddings in Excel. Building RNNs
8. Exotic CNN architecures. RNN from scratch