tf.image.resize_images has aliasing when downsampling and does not have gradients for bicubic mode. This implementation fixes those problems.
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Updated
May 3, 2019 - Python
tf.image.resize_images has aliasing when downsampling and does not have gradients for bicubic mode. This implementation fixes those problems.
PyTorch implementation of the mixture distribution family with implicit reparametrisation gradients.
Neural network visualization tool after an optional model compression with parameter pruning: (integrated) gradients, guided/visual backpropagation, activation maps for the cao model on the IndianPines dataset
An automatic differentiation library written in Python with NumPy vectorization.
This tool can be used to find the most influential words on a document. We define most influential as the words that influence a trained classifier the most to give it a particular classification.
Software pipeline to identify lane boundaries from a video streaming from a front-facing camera on a car using color transform and gradient
Predicting wine quality using regression on the well-known UCI data set and more
Avoiding the vanishing gradients problem by adding random noise and batch normalization
First assignment in ׳Deep Learning for Texts and Sequences' course (using NumPy only) by Prof. Yoav Goldberg at Bar-Ilan University
Python Library for creating and training CNNs. Implemented from scratch.
A pipeline to detect road lane lines in images from a monocular camera
Explore the Math behind it by designing a neural network, derive the parameter gradients with respect to loss function and update the parameter weights and update the weight parameters using the gradients without the help of in-built libraries.
Take a list of rgb color values and insert a number of colors of your choice in between each given color pair in a smooth transition.
Advanced lane detection (incl. curvature) using advanced computer vision techniques.
Advanced Lane Finding (project 2 of 9 from Udacity Self-Driving Car Engineer Nanodegree)
Demonstrate how to do backpropagation using an example of BatchNorm-Sigmoid-MSELoss network with a detailed derivation of gradients and custom implementations.
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