The fourth course, Convolutional Neural Networks of Deeplearning.ai has now been released on coursera. People have been waiting for this one, but i think that the delay was to make the material very up to date with current research results. The four weeks of learning deals with:
- Foundations of Convolutional Neural Networks
- Deep convolutional models: case studies
- Object detection
- Special applications: Face recognition & Neural style transfer
The “YOLO9000: Better, Faster, Stronger” paper describes the improvements to the YOLO, You only look once, architecture that enables realtime object detection and classification. It can classify over 9000 object categories and outperforms Faster RCNN with ResNet and SSD while being significantly faster. They train on both COCO dataset for detection simultaneously with ImageNet for Classification and combine it with a wordtree so that they can also fallback to “dog” if they cannot classify for instance a specific dog breed.
The first version, and architecture can be seen in this paper.
Here is a video presentation: https://www.youtube.com/watch?v=GBu2jofRJtk
The video lectures for Stanfords very popular CS231n (Convolutional Neural Networks for Visual Recognition) that was held in Spring 2017 was released this month. (According to their twitter page, the cs231n website gets over 10 000 views per day. The reading material on their page is really good at explaining CNNs)
Here are the video lectures:
These are the assignments for the course:
- Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network
- Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets
- Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
Also Make sure to check out last years student reports. note: one is about improving the state of the art of detecting the Higgs Boson.