Type safe computational graph interface
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Updated
Jun 1, 2022 - Java
Type safe computational graph interface
a compact tensor library capable of training deep neural networks on both cpu and cuda devices
Code for the part 2 of the tutorial on pychain
Predict the number of bikeshare users on a given day by building my own deep-learning library.
Cached lazy evaluation of computational graphs
A simple library for building computational graphs with autodiff support.
An aggregation of my experiments in Neural Networks and Deep Learning using TensorFlow.
Code for the part 1 of the tutorial on pychain
A simple mimicking of TensorFlow, which including forward and backward propogation.
TensorFlow's very distant and not so bright cousin
RNN in Julia for MNIST digit recognition implemented with automatic differentiation. Over 96% accuracy.
Yet another tensor automatic differentiation framework
Library to manipulate tensors on the GPU.
Distributed Algebraic Computations
A GPU-parallel Java automatic differentiation computational graph implementation.
Network-wide estimation of traffic flow and travel time with data-driven macroscopic models
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