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Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Reference models and tools for Cloud TPUs.
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
A set of Deep Reinforcement Learning Agents implemented in Tensorflow.
Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)
Experiments with Deep Learning
Dynamic seq2seq in TensorFlow, step by step
This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).
A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.
The Official Repository for "Generalized OOD Detection: A Survey"
Tutorial on normalizing flows.
Realistic Handwriting with Tensorflow
This repository contains all the material for the MLTrain NIPS workshop
Building Agents with Imagination: pytorch step-by-step implementation
Object detection and segmentation for a surgery robot using Mask-RCNN on Python 3, Keras, and TensorFlow..
Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.
fast implementation of singular spectrum transformation (change point detection algorithm)
Code to replicate the key results from Exploring the Limits of Out-of-Distribution Detection (https://arxiv.org/abs/2106.03004) by Stanislav Fort, Jie Ren, Balaji Lakshminarayanan, published at Neu…
Vision-based off-road navigation with geographical hints
Out-of-distribution detection using the pNML regret. NeurIPS2021
Change point detection by using density ratio estimation
Adversarial Images for Variational Autoencoders
Using pre-trained DL models and Transformations for generating occupancy maps. Includes some other basic deep learning tasks. Feel free to contribute.