Notes for the research paper Deep Residual Learning for Image Recognition
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Notes for the paper Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation.
Notes for the research paper Going Deeper With Convolutions.
Notes for the research paper Very Deep Convolutional Networks for Large-Scale Image Recognition.
Notes for the research paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting.
Notes for the Chapter Probability and Information Theory of the Deep Learning Book.
Notes for the research paper Net2Net: Accelerating Learning via Knowledge Transfer.
Notes for the Chapter Linear Algebra of the Deep Learning Book.
Jonas Mueller, Aditya Thyagarajan, AAAI-2016
This post looks at some of the methods used for Image Segmentation
This post provides an explanation of Binarized Neural Networks, a low memory and computationally cheap architecture perfect for very small devices.
Image to Image translation using BicycleGANs.
This post provides an overview of the two most common generative models: GANs and VAEs.
A complete guide on how to start off with Deep Learning