openai
/

gpt-oss-20b

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+      "readme_snippet": "Machine Learning Notebooks\n==========================\n\n# \u26a0 The 3rd edition of my book will be released in October 2022. The notebooks are available at [ageron/handson-ml3](https://github.com/ageron/handson-ml3) and contain more up-to-date code.\n\nThis project aims at teaching you the fundamentals of Machine Learning in\npython. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/):\n\n<img src=\"https://images-na.ssl-images-amazon.com/images/I/51aqYc1QyrL._SX379_BO1,204,203,200_.jpg\" title=\"book\" width=\"150\" />\n\n**Note**: If you are looking for the first edition notebooks, check out [ageron/handson-ml](https://github.com/ageron/handson-ml). For the third edition, check out [ageron/handson-ml3](https://github.com/ageron/handson-ml3).\n\n## Quick Start\n\n### Want to play with these notebooks online without having to install anything?\nUse any of the following services (I recommended Colab or Kaggle, since they offer free GPUs and TPUs).\n\n**WARNING**: _Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about._\n\n* <a href=\"https://colab.research.google.com/github/ageron/handson-ml2/blob/master/\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n\n* <a href=\"https://homl.info/kaggle/\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open in Kaggle\" /></a>\n\n* <a href=\"https://mybinder.org/v2/gh/ageron/handson-ml2/HEAD?filepath=%2Findex.ipynb\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Launch binder\" /></a>\n\n* <a href=\"https://homl.info/deepnote/\"><img src=\"https://deepnote.com/buttons/launch-in-deepnote-small.svg\" alt=\"Launch in Deepnote\" /></a>\n\n### Just want to quickly look at some notebooks, wi",
+      "license_snippet": "                                 Apache License\n                           Version 2.0, January 2004\n                        http://www.apache.org/licenses/\n\n   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n   1. Definitions.\n\n      \"License\" shall mean the terms and conditions for use, reproduction,\n      and distribution as defined by Sections 1 through 9 of this document.\n\n      \"Licensor\" shall mean the copyright owner or entity authorized by\n      the copyright owner that is granting the License.\n\n      \"Legal Entity\" shall mean the union of the acting entity and all\n      other entities that control, are controlled by, or are under common\n      control with that entity. For the purposes of this definition,\n      \"control\" means (i) the power, direct or indirect, to cause the\n      direction or management of such entity, whether by contract or\n      otherwise, or (ii) ownership of fifty percent (50%) or more of the\n      outstanding shares, or (iii) beneficial ownership of such entity.\n\n      \"You\" (or \"Your\") shall mean an individual or Legal Entity\n      exercising permissions granted by this License.\n\n      \"Source\" form shall mean the preferred form for making modifications,\n      including but not limited to software source code, documentation\n      source, and configuration files.\n\n      \"Object\" form shall mean any form resulting from mechanical\n      transformation or translation of a Source form, including but\n      not limited to compiled object code, generated documentation,\n      and conversions to other media types.\n\n      \"Work\" shall mean the work of authorship, whether in Source or\n      Object form, made available under the License, as indicated by a\n      copyright notice that is included in or attached to the work\n      (an example is provided in the Appendix below).\n\n      \"Derivative Works\" shall mean any work, whether in Source or Object\n      form, that is based on (or derived from) the Work and for which the\n      editori",
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+      "readme_snippet": "# PyTorch Examples\n\nhttps://pytorch.org/examples/\n\n`pytorch/examples` is a repository showcasing examples of using [PyTorch](https://github.com/pytorch/pytorch). The goal is to have curated, short, few/no dependencies _high quality_ examples that are substantially different from each other that can be emulated in your existing work.\n\n- For tutorials: https://github.com/pytorch/tutorials\n- For changes to pytorch.org: https://github.com/pytorch/pytorch.github.io\n- For a general model hub: https://pytorch.org/hub/ or https://huggingface.co/models\n- For recipes on how to run PyTorch in production: https://github.com/facebookresearch/recipes\n- For general Q&A and support: https://discuss.pytorch.org/\n\n## Available models\n\n- [Image classification (MNIST) using Convnets](./mnist/README.md)\n- [Word-level Language Modeling using RNN and Transformer](./word_language_model/README.md)\n- [Training Imagenet Classifiers with Popular Networks](./imagenet/README.md)\n- [Generative Adversarial Networks (DCGAN)](./dcgan/README.md)\n- [Variational Auto-Encoders](./vae/README.md)\n- [Superresolution using an efficient sub-pixel convolutional neural network](./super_resolution/README.md)\n- [Hogwild training of shared ConvNets across multiple processes on MNIST](mnist_hogwild)\n- [Training a CartPole to balance with actor-critic](./reinforcement_learning/README.md)\n- [Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext](snli)\n- [Time sequence prediction - use an LSTM to learn Sine waves](./time_sequence_prediction/README.md)\n- [Implement the Neural Style Transfer algorithm on images](./fast_neural_style/README.md)\n- [Reinforcement Learning with Actor Critic and REINFORCE algorithms on OpenAI gym](./reinforcement_learning/README.md)\n- [PyTorch Module Transformations using fx](./fx/README.md)\n- Distributed PyTorch examples with [Distributed Data Parallel](./distributed/ddp/README.md) and [RPC](./distributed/rpc)\n- [Several examples illustrating the C++ Frontend](cpp)\n- [I",
+      "license_snippet": "BSD 3-Clause License\n\nCopyright (c) 2017, Pytorch contributors\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n  list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n  this list of conditions and the following disclaimer in the documentation\n  and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n  contributors may be used to endorse or promote products derived from\n  this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n",
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