pytorch-GAT
gan-vae-pretrained-pytorch
| pytorch-GAT | gan-vae-pretrained-pytorch | |
|---|---|---|
| 14 | 1 | |
| 2,222 | 162 | |
| - | - | |
| 0.0 | 0.0 | |
| over 1 year ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
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pytorch-GAT
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[D] Graph neural networks
This repo has a nice hands-on walkthrough: https://github.com/gordicaleksa/pytorch-GAT
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[Discussion] Who are some good deep learning YouTubers?
Maybe: https://youtube.com/c/TheAIEpiphany
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Any resources to learn MDPs and finally complex POMDPs?
I would suggest these three resources: 1: Richard Sutton’s book 2:Reinforcement learning lecture series by deep mind 3:Deep RL lectures by deep mind 4: spinning up by open Ai -> very good resource for key research papers. Try to read and then implement them 5: Practical RL channel by machine learning with Phil. -> good resource on getting to know how to implement deep RL algorithms 6: Research paper breakdown/ practical DL -> good channel to follow for how to understand latest research papers
- Open-source Graph Attention Network (GAT) project with transductive + inductive Jupiter notebooks !
- Obrazovanje
- Open-source Graph Attention Network (GAT) project with transductive + inductive Jupiter notebooks!
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Graph Attention Network project walkthrough! (recently open-sourced <3 )
GitHub link: https://github.com/gordicaleksa/pytorch-GAT
- Show HN: I'm Open-Sourcing Graph Attention Network (GAT) PyTorch
gan-vae-pretrained-pytorch
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DCGAN (CIFAR-10) Generating fake images is easy, but how to also output the class label (1 to 10) with the fake generated images?
I have this DCGAN model (https://github.com/csinva/gan-vae-pretrained-pytorch/tree/master/cifar10_dcgan) which generates fake Cifar-10 images. However I also want to get the intended class label output with the fake generated images. How can I do this? This model which I found only generates fake images but doesn't know what class the generated images belong to.
What are some alternatives?
pytorch-seq2seq - Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
AvatarGAN - Generate Cartoon Images using Generative Adversarial Network
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
TokenCut - (CVPR 2022) Pytorch implementation of "Self-supervised transformers for unsupervised object discovery using normalized cut"
dnn_from_scratch - A high level deep learning library for Convolutional Neural Networks,GANs and more, made from scratch(numpy/cupy implementation).
pytorch-learn-reinforcement-learning - A collection of various RL algorithms like policy gradients, DQN and PPO. The goal of this repo will be to make it a go-to resource for learning about RL. How to visualize, debug and solve RL problems. I've additionally included playground.py for learning more about OpenAI gym, etc.
JoJoGAN - Official PyTorch repo for JoJoGAN: One Shot Face Stylization