choudharyharsh122/humanCompatible
Fairness-constrained training of machine learning models in TensorFlow, PyTorch, and beyond (⭐ 0 | Jupyter Notebook)
Rainnie-oo7/Action-Recognition-FPHAB-GCN
[should be linear at the end for classification] graph convolution mdoel with Pytorch GCN, to output an action (movement) of a hand, by reading in skeleton datae 21 joints, without images, FPHA Dataset, in given 45 Classes, such es opening a soap bottle, a soda can, and few\many…
jaehyun513/STUNT
Official PyTorch implementation of STUNT: Few-shot Tabular Learning with Self-generated Tasks from Unlabeled Tables (ICLR 2023 Spotlight). (⭐ 60)
amaljoseph/EndToEnd_Signature-Detection-Cleaning-Verification_System_using_YOLOv5-and-Cycl…
An end-to-end signature verification system to extract, clean and verify signatures in documents. Signatures are detected using YOLOv5. Noise is cleaned using a CycleGAN approach and verified. Keras / Tensorflow / PyTorch (⭐ 196 | Jupyter Notebook)
bprabhakar/upside-down-reinforcement-learning
Pytorch based implementation of Upside Down Reinforcement Learning (UDRL) by J. Schmidhuber et al. (⭐ 11 | Jupyter Notebook)
Computational graph vs (computer algebra) symbolic expression
Tags: tensorflow, pytorch, theano, backpropagation, automatic-differentiation | Score: 5 | Answered: Yes
BY571/Upside-Down-Reinforcement-Learning
Upside-Down Reinforcement Learning (⅂ꓤ) implementation in PyTorch. Based on the paper published by Jürgen Schmidhuber. (⭐ 78)
haron1100/Upside-Down-Reinforcement-Learning
Implementation of Schmidhuber's Upside Down Reinforcement Learning paper in PyTorch (⭐ 27 | Jupyter Notebook)
ctallec/world-models
Reimplementation of World-Models (Ha and Schmidhuber 2018) in pytorch (⭐ 658 | Python)
wangg12/intel-cervical-cancer
Team GuYuShiJie~'s 15th (top 2%) solution of cervix type classification in Kaggle 2017 competition, using PyTorch. (⭐ 19)
Deep Learning Containers documentation | Google Cloud Documentation
Prepackaged and optimized deep learning containers for developing, testing, and deploying AI applications on TensorFlow, PyTorch, and scikit learn.
Deep Learning Containers documentation | Google Cloud Documentation
Prepackaged and optimized deep learning containers for developing, testing, and deploying AI applications on TensorFlow, PyTorch, and scikit learn.