Imaging in deep learning for computer vision and classification problems have become a compelling way to impress friends and family, influence strangers and demonstrate the power and adaptability of artifical neural networks and convolutional networks...
Pattern Exploration was pleased to present some AI research at this year, 2019’s Society for Imaging Informatics in Medicine, SIIM conference in Denver, CO. Our presentation, titled “A Two-Stage Deep Learning Approach to Chest X-Ray Analysis” Pattern Exploration Presentation Our research and presentation highlighted PE’s fundamental philosophy about small, interpretable neural networks that buck the [...]
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Comparing Numpy, Pytorch, and autograd on CPU and GPU¶ by Chuck Anderson, Pattern Exploration 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 Autograd Pytorch with autograd on GPU Wrapped up in one [...]
Numpy versus Pytorch¶ by Chuck Anderson, Pattern Exploration Here 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 are compared. The Adam optimization algorithm in numpy and pytorch are compared, as well as the Scaled Conjugate Gradient optimization algorithm [...]