ANN, RL, Gaussian process
In ML-Projects-From-Scratch I explore the mathematics behind various machine learning algorithms by building them myself and doing some simple predictions.
ML-Projects-From-Scratch is tested to work under Python 3
A artificial neural network implemented from scratch. I learn the weights and biases through batch gradient descent. I test the ANN by identifying handwritten numbers (a classic data set found here Number data set).
The backward propagation,
Reinforcement learning implented from scratch. I investigate the use of combined modules having their own Q-table. I apply Gibbs policy improvement. I apply this to an environment containing various rewards.
Gibbs policy improvement,
Q-table update,
Gaussian process used to predict time-series data for motion movement. The hyper parameters are found by maximizing the log likelyhood function
where Q is the kernel given by,
I use steepest ascent to find the hyper parameters,
Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York
Excelent list of free, open source books on machine learning, statistics, data-mining, etc.