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Comparing Numpy, Pytorch, and autograd on CPU and GPU

Code for fitting a polynomial to a simple data set is discussed. Implementations in numpy, pytorch, and autograd on CPU and GPU are compred. This post is available for downloading as this jupyter notebook. Table of Contents Very Brief Introduction to Autograd Using Numpy to Fit a Polynomial to Data Now, with Pytorch Pytorch with [...]

Numpy versus Pytorch

Here we compare the accuracy and computation time of the training of simple fully-connected neural networks using numpy and pytorch implementations and applied to the MNIST data set. The Adam optimization algorithm in numpy and pytorch are compared, as well as the Scaled Conjugate Gradient optimization algorithm in numpy.The original notebook is available here.Additional comments and [...]

Fast Reinforcement Learning After Pretraining

This article is available here as a jupyter notebook. We presented at IJCNN, 2015 the following paper, which won the Best Paper Award Anderson, C., Lee, M., and Elliott, D., “Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics“, Proceedings of the IJCNN, 2015, Killarney, Ireland. Abstract: Deep learning algorithms have recently appeared [...]