Gan Tutorial
🎨 tutorial materials for the lab seminar in tensorflow
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Examples | 7,595 | 3 months ago | 956 | July 07, 2022 | 123 | apache-2.0 | Jupyter Notebook | |||
| TensorFlow examples | ||||||||||
| Tensorflow | 2,575 | 6 months ago | 1,618 | other | Jupyter Notebook | |||||
| Project containig related material for my TensorFlow articles | ||||||||||
| Tensorflow Tutorial | 1,983 | 7 years ago | 2 | Jupyter Notebook | ||||||
| Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE | ||||||||||
| Tensorflow Workshop | 625 | 6 years ago | 10 | apache-2.0 | Jupyter Notebook | |||||
| This repo contains materials for use in a TensorFlow workshop. | ||||||||||
| Megnet | 444 | a year ago | 21 | bsd-3-clause | Jupyter Notebook | |||||
| Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals | ||||||||||
| Jarvis | 260 |  | 13 | 4 months ago | 100 | September 22, 2023 | 45 | other | Python | |
| JARVIS-Tools: an open-source software package for data-driven atomistic materials design. Publications: https://scholar.google.com/citations?user=3w6ej94AAAAJ | ||||||||||
| Bcs_workshop_apr_20 | 133 | 4 years ago | 1 | mit | Jupyter Notebook | |||||
| Workshop on basic machine learning, computational modeling, psychophysics, basic data analysis and experiment design | ||||||||||
| Copper_price_forecast | 73 | 6 years ago | gpl-3.0 | Python | ||||||
| copper price(time series) prediction using bpnn and lstm | ||||||||||
| Matdgl | 63 | 2 years ago | bsd-2-clause | Python | ||||||
| MatDGL is a neural network package that allows researchers to train custom models for crystal modeling tasks. It aims to accelerate the research and application of material science. | ||||||||||
| Conf_tensorflow_training_day2 | 24 | 5 years ago | 1 | HTML | ||||||
| TensorFlow training at RStudio::conf(2019) | ||||||||||
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