Vithursan Thangarasa's repositories
MagnetLoss-PyTorch
PyTorch implementation of a deep metric learning technique called "Magnet Loss" from Facebook AI Research (FAIR) in ICLR 2016.
terraform-aws-spotgpu
Fully automated provisioning of AWS EC2 Spot Instances for Deep Learning workloads using Terraform.
nanoGPT_mlx
Port of Andrej Karpathy's nanoGPT to Apple MLX framework.
VAE-Gumbel-Softmax
An implementation of a Variational-Autoencoder using the Gumbel-Softmax reparametrization trick in TensorFlow (tested on r1.5 CPU and GPU) in ICLR 2017.
deep-learning-conda-envs
This repo contains Anaconda environment.yml files for different deep learning frameworks (i.e. TensorFlow, PyTorch, Theano, etc.)
SPL-ADVisE
PyTorch code for BMVC 2018 paper: "Self-Paced Learning with Adaptive Visual Embeddings"
Ackermann-Quicksort-Experiments
Analysis of recursion in C and Python for the Ackermann Function and Quicksort Algorithm.
Roadside-Monitoring-System
Capstone Project: A Solar-Powered Roadside Monitoring Systems in Construction Zones.
Seq2Seq_PlouffeRainbows
This repository contains sequence-to-sequence (seq2seq) code in Tensorflow that can be used as a tool for non-linear regression analysis or more specifically time-series analysis where there are no class-labels.
Automatic_Speech_Recognition
End-to-end automatic speech recognition from scratch in Tensorflow
awesome-deeplearning-resources
Deep Learning and deep reinforcement learning research papers and some codes
blogposts
Repository for all posts by Mad Eden S.L
ContinuousAI
Continuous AI collaborative project
deep-learning-papers
This repository contains short summaries of some deep learning papers.
GradientEpisodicMemory
Continuum Learning with GEM: Gradient Episodic Memory
learning-to-learn
Learning to Learn in TensorFlow
maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.
SimplifiedTransformers_mlx
Apple MLX port of the Simplified Transformer Architecture (2023)
vim-config
Vim Configuration
vimrc
My vim environment.