30 Jun 2019
In the last reinforcement learning blog post we covered dynamic programming methods. In this blog post we will cover Monte Carlo (MC) methods. The biggest difference between these two methods is that dynamic programming methods assume a complete knowledge of the environement (via a MDP), but Monte Carlo methods do not. Instead, with Monte Carlo methods, knowledge of the environment is learned through experience. Another significant difference is that Monte Carlo methods can only learn from episodic tasks i.e. ones that start and terminate.
29 Jun 2019
This series of blog posts is intended to be a collection of short, concise, cheat-sheet-like notes on different topics relating to reinforcement learning. This first one will cover dynamic programming methods applied to reinforcement learning.
14 May 2019
Numpy is the go-to library for linear algebra computations in python. It is a highly optimized library that uses BLAS as well as SIMD vectorization resulting in very fast computations. Having said that, there are times when it is preferable to perform linear algebra computations on the GPU i.e. using CUDA’s cuBLAS linear algebra library. For example, the linear algebra computations associated with training large deep neural networks are commonly performed on GPU. In cases like these, the vectors and matrices are so large that the parallelization offerred by GPUs allows them to outperform linear algebra libraries like numpy.
05 Mar 2019
This blog post will cover a CUDA C implementation of the K-means clustering algorithm. K-means clustering is a hard clustering algorithm which means that each datapoint is assigned to one cluster (rather than multiple clusters with different probabilities). The algorithm starts with random cluster assignments and iterates between two steps
14 Jul 2018
Many deep learning libraries like TensorFlow use graphs to represent the computations involved in neural networks. Not only are these graphs used to compute predictions for a given input to the network but they are also used to backpropagate gradients during the training phase. The main advantage of this graph representation is that each computation can be encapsulated as a node on the graph that only cares about its input and output. This level of abstraction gives you the flexibility to build neural networks of (nearly) arbitrary sizes and shapes (eg. MLPs, CNNs, RNNs, etc.). This blog post will implement a very basic version of a computational graph engine.