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
Here is a good tutorial explaining how convolutions work:
Here is an example of a convolution with half (one) padding and stride 2
The Energy-Efficient Multimedia Systems (EEMS) group at MIT has a Tutorial on Hardware Architectures for Deep Neural Networks. Here is the website: http://eyeriss.mit.edu/tutorial.html
All Slides in one PDF.
The slides explain a lot of convolutional Nerual Networks and how they work. It also describes many different topics ranging from the architectures for the winners of ImageNet and how their success also correlate with the use of GPUs for processing. The later part is more on the details in computation, what is computed where and how.
If you wish to check out each individual topic, they are also splitted into several different slides:
- Background of Deep Neural Networks [ slides ]
- Survey of DNN Development Resources [ slides ]
- Survey of DNN Hardware [ slides ]
- DNN Accelerator Architectures [ slides ]
- Advanced Technology Opportunities [ slides ]
- Network and Hardware Co-Design [ slides ]
- Benchmarking Metrics [ slides ]
- Tutorial Summary [ slides ]
- References [ slides ]
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.
In this recently published paper, Jianxin Wu helps the reader understand
how a CNN runs at the mathematical level. It is self contained and you should not need any further material to understand it from a mathematical viewpoint.
With CNN, the important part is understanding what happens when you adjust the different parameters. Bu in order to make sense of those it is much easier when you know the underlying principles behind it.
Here you go
Here is another book on deep learning and neural nets:
Neural Networks and Deep Learning by Michael Nielsen / May 2017
The other one i mentioned before is the Deep Learning Book
You can read both for free online.
Read these articles:
and then read these papers by Ian Goodfellow et al:
You can also watch this video by Ian Goodfellow: