## Application Tutorials: - [Digital Image Processing Using MATLAB, 2nd edition](http://www.imageprocessingplace.com/DIPUM-2E/dipum2e_main_page.htm), ([Amazon](http://www.amazon.com/Digital-Image-Processing-Using-MATLAB/dp/0982085400)), by Rafael C. Gonzalez. > You could find corresponding Matlab implementations based on the above theory-oriented version. - [OpenCV 2 Computer Vision Application Programming Cookbook](http://www.laganiere.name/opencvCookbook/), ([Amazon](http://www.amazon.com/OpenCV-Computer-Application-Programming-Cookbook/dp/1849513244)), by Robert Laganiere. > You might also be interested in *Mastering OpenCV with Practical Computer Vision Projects*, by Daniel Baggio, if you want to build some fancy projects based on Android, iOS and Microsoft Kinect.

## Math Review: - [Linear Algebra Done Right](http://linear.axler.net/), ([Amazon](http://www.amazon.com/Linear-Algebra-Right-Undergraduate-Mathematics/dp/0387982582)), by Sheldon Axler. > The [Open Course](http://www-math.mit.edu/~gs/) from Prof. Gilbert Strang is also a good supplementary material for self-study. - [Probability and Stochastic Processes](http://www.wiley.com/WileyCDA/WileyTitle/productCd-EHEP000391.html), ([Amazon](http://www.amazon.com/Probability-Stochastic-Processes-Introduction-Electrical/dp/0471272140)), by Roy D. Yates. > As it claims, a friendly introduction for Electrical and Computer Engineers.

## MOOC on Coursera: - [Image and video processing](https://class.coursera.org/images-002), by Guillermo Sapiro. >Beginner friendly. - [Machine Learning](https://class.coursera.org/ml-005), by Andrew Ng. >Great course, actually every beginner should take it.

## Courses and Notes: - [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/), by Fei-Fei Li in Stanford University. >Great course for basic ideas of Deep Learning. - [VGG Convolutional Neural Networks Practical](http://www.robots.ox.ac.uk/~vgg/practicals/cnn/), and [More Practicals](http://www.robots.ox.ac.uk/~vgg/practicals/overview/index.html), by Andrea Vedaldi and Andrew Zisserman. >Useful practical for Deep Learning based on Matlab.

## Extra Machine Learning Tutorial: - [Machine Learning: a Probabilistic Perspective](http://www.cs.ubc.ca/~murphyk/MLbook/), ([Amazon](http://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020)), by Kevin Patrick Murphy. > More suitable for beginners than PRML from Prof. Bishop. It also provides all the [Matlab implementations](https://github.com/probml/pmtk3) from which you will benifit a lot. - [Machine Learning in Action](http://www.manning.com/pharrington/), ([Amazon](http://www.amazon.com/Machine-Learning-Action-Peter-Harrington/dp/1617290181)), Peter Harrington. > Interesting machine learning tutorial based on Python. You can implement some tiny real systems quickly and be happy.