evcu

Google Brain, Montreal M.Sc at NYU Courant. Machine Learning Learner.

@googleMontreal

Pinned Repositories

Awesome-Pruning
A curated list of neural network pruning resources.
2 2 00
cuda-sudoku-solver
Language:Cuda7 2 01
cv2016
Homeworks for Computer Vision Class given by Rob Fergus in 2016 Fall
Language:Jupyter Notebook5 2 10
evcu.github.io
Language:HTML1 2 01
exp.bootstrp
This repo is a bootstrap for experiments and includes helper functions scripts for pytorch training and slurm job scheduler.
Language:Python2 2 00
FML-FA16-Project
Language:OpenEdge ABL0 2 00
kmeans-openmpi
Open-mpi implementation of K-means.
Language:Jupyter Notebook4 3 03
numpy_autograd
a simple implementation of autograd engine
Language:Jupyter Notebook24 4 13
pytorchpruner
Language:Python23 2 03
Short-Distance-Stepping
Short distance human stepping includes a lot of variants. Stance type, stepping length, location, turning side, step type are some of these variants which make short distance human stepping hard to model. Prototyping and understanding short distance human stepping is quite important for virtual prototyping human locomotion. The solution to tackle this problem should be accurate and fast. In the experiment made by Mr. Pisupati, marker data is converted into 2-D body and feet trajectories.
Language:Matlab1 2 00

evcu's Repositories

evcu/numpy_autograd
a simple implementation of autograd engine
Language:Jupyter Notebook24 4 13
evcu/pytorchpruner
Language:Python23 2 03
evcu/cuda-sudoku-solver
Language:Cuda7 2 01
evcu/cv2016
Homeworks for Computer Vision Class given by Rob Fergus in 2016 Fall
Language:Jupyter Notebook5 2 10
evcu/kmeans-openmpi
Open-mpi implementation of K-means.
Language:Jupyter Notebook4 3 03
evcu/Awesome-Pruning
A curated list of neural network pruning resources.
2 2 00
evcu/exp.bootstrp
This repo is a bootstrap for experiments and includes helper functions scripts for pytorch training and slurm job scheduler.
Language:Python2 2 00
evcu/evcu.github.io
Language:HTML1 2 01
evcu/Short-Distance-Stepping
Short distance human stepping includes a lot of variants. Stance type, stepping length, location, turning side, step type are some of these variants which make short distance human stepping hard to model. Prototyping and understanding short distance human stepping is quite important for virtual prototyping human locomotion. The solution to tackle this problem should be accurate and fast. In the experiment made by Mr. Pisupati, marker data is converted into 2-D body and feet trajectories.
Language:Matlab1 2 00
evcu/FML-FA16-Project
Language:OpenEdge ABL0 2 00
evcu/bigdata2017
Language:Jupyter Notebook2 01
evcu/circuitpython_projects
Some qController programming
Language:Python2 0
evcu/Crypto2017fall
Language:Jupyter Notebook3 0
evcu/ds1004
4 0
evcu/feds
FEDS for Facial Expression Detection System
Language:C2 0
evcu/hpc2017
Language:Jupyter Notebook2 0
evcu/lists
Some lists for good
2 0
evcu/meta-dataset
Language:Python3 0
evcu/MetaOptNet
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
Language:Python2 0
evcu/micrograd
A tiny autograd engine and a neural net library on top of it, potentially for educational purposes
Language:Jupyter Notebook1 0
evcu/Nalwe
Results of various trained w2v models.
Language:C2 0
evcu/nn
Language:Lua2 0
evcu/playground
Play with neural networks!
Language:TypeScript1 0
evcu/propertycrawler
Property Advert Crawler
Language:Matlab2 0
evcu/pyprobml
Python code for "Machine learning: a probabilistic perspective" (2nd edition)
Language:Jupyter Notebook1 01
evcu/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Language:Python3 0
evcu/rigl
End-to-end training of sparse deep neural networks with little-to-no performance loss.
Language:Python2 0
evcu/Sparse-Building-Blocks-DNNs
Comparison of sparse vs. dense matrix encodings for pruned fully connected and convolutional neural network layers on a GPU
Language:C2 0