PyTorch

@PyTorch

pytorch.org
Vrijeme pridruživanja: rujan 2016.
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  1. 30. sij

    Welcome OpenAI to the PyTorch community!

    We're standardizing OpenAI's deep learning framework on PyTorch to increase our research productivity at scale on GPUs (and have just released a PyTorch version of Spinning Up in Deep RL):
    163 proslijeđena tweeta 846 korisnika označava da im se sviđa
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  2. proslijedio/la je Tweet
    28. sij

    I'm happy to announce our latest work on self-supervised learning for . PASE+ is based on a multi-task approach useful for recognition. It will be presented at . paper: code: @Mila

    7 replies 82 proslijeđena tweeta 302 korisnika označavaju da im se sviđa
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  3. 28. sij

    Check out a fresh take on frontends from the creators of

    Introducing the new Thinc, a refreshing functional take on deep learning! 🔮 Static type checking 🔥 Mix , & ⛓️ Integrated config system 🧮 Extensible backends incl. JAX (experimental) 🧬 Variable-length sequences & more
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    1 reply 21 proslijeđeni tweet 115 korisnika označava da im se sviđa
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    28. sij

    We are proud to announce the release of Catalyst v20.01.3, DL/RL framework for - core architecture redesign - improved registry – Albumentations and SMP support - MultiPhaseRunner and GanRunner Working hard for Catalyst.RL 2.0 🚀

    1 reply 38 proslijeđenih tweetova 109 korisnika označava da im se sviđa
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    27. sij

    Kornia v0.2.0 is out ! We have introduced a new data augmentation module with strong GPU support, extended the set of color conversion algorithms, supporting GPU CI tests with v1.4.0, and much more. Happy coding !

    163 proslijeđena tweeta 517 korisnika označava da im se sviđa
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  6. 26. sij

    Thank You for posting 10,000 replies on the forums and helping many hundreds of us!

    I just posted my 10,000th reply in the discuss forum! Thanks everyone for creating such a great community, for the guidance and mentorship I received, and for starting this journey.
    1 reply 7 proslijeđenih tweetova 195 korisnika označava da im se sviđa
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  7. proslijedio/la je Tweet
    22. sij

    code showing the generic learning setup and reproducing simple experiments is now available! Code: Project Page: Paper:

    Excited to release our work on MetaSim which learns to generate synthetic datasets that resemble real datasets in content. Antonio Torralba etc paper: project page:
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    34 proslijeđena tweeta 160 korisnika označava da im se sviđa
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  8. proslijedio/la je Tweet
    22. sij

    OpenMined + collaborating to advance open source software development. Learn about these talented teams on our blog: Many opportunities ahead. Join our Slack community to find out more!

    51 proslijeđeni tweet 160 korisnika označava da im se sviđa
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  9. proslijedio/la je Tweet
    17. sij

    Pyro 1.2 release adds poutine.reparam to rewrite models to improve geometry via: - neural transport - discrete cosine transform - auxiliary variable methods for Levy Stable distributions - conditional Gaussian HMMs - decentering - transform unwrapping

    42 proslijeđena tweeta 184 korisnika označavaju da im se sviđa
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  10. proslijedio/la je Tweet
    17. sij

    To make my research more reproducible, extensible and comparable to that of others & out of need to homogenize the language we use to express nn pruning methods, I contributed `nn.utils.prune` to 1.4 (see highlights ) Try it out & build on it! 🔥

    56 proslijeđenih tweetova 325 korisnika označava da im se sviđa
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  11. 16. sij

    torchtext v0.5: improved data API, unsupervised text tokenization - bindings for SentencePiece - New: enwiki9, revisions to PennTreebank, WikiText103, WikiText2, IMDb (looking for feedback) Release Notes:

    1 reply 33 proslijeđena tweeta 139 korisnika označava da im se sviđa
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  12. 16. sij

    torchaudio v0.4: more transforms, datasets, backend support - LibriSpeech and Common Voice loaders - Filters (biquad), batched / jittable transforms (MFCC, gain, dither), more augmentation - interactive speech recognition demo with voice detection

    76 proslijeđenih tweetova 295 korisnika označava da im se sviđa
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  13. 16. sij

    torchvision v0.5: quantization, production - ResNets, MobileNet, ShuffleNet, GoogleNet and InceptionV3 now have quantized counterparts with pre-trained models, scripts for quantization-aware training. - All models are TorchScript-ready and ONNX-ready

    1 reply 73 proslijeđena tweeta 221 korisnik označava da mu se sviđa
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  14. 15. sij

    first release for Python 3.8: binaries for the entire matrix of Python 3.8 {pytorch, torchvision, torchaudio} configurations will be live by Jan 23rd. Some configurations are live already

    9 proslijeđenih tweetova 40 korisnika označava da im se sviđa
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  15. 15. sij

    v1.4: customizable mobile builds, Distributed Model Parallelism via experimental RPC API, Java Bindings, Chaining LRSchedulers Summary: Release Notes: Last release for Python 2 (bye bye!)

    1 reply 148 proslijeđenih tweetova 377 korisnika označava da im se sviđa
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  16. proslijedio/la je Tweet
    15. sij

    PyTorch Metric Learning now available on Anaconda! Installation: "conda install pytorch-metric-learning -c metric-learning" View more details here:

    179 proslijeđenih tweetova 486 korisnika označava da im se sviđa
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  17. proslijedio/la je Tweet
    14. sij

    [News] Preferred Networks releases Optuna v1.0, the first major version of the open-source hyperparameter optimization framework for machine learning. Optimize your optimization.

    123 proslijeđena tweeta 283 korisnika označavaju da im se sviđa
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  18. 14. sij

    Learn how to automate most of the infrastructure work required to deploy PyTorch models in production using Cortex, an open source tool for deploying models as APIs on AWS.

    90 proslijeđenih tweetova 334 korisnika označavaju da im se sviđa
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  19. proslijedio/la je Tweet
    13. sij

    I published a new article on the blog: Active Transfer Learning with PyTorch. Read about adapting Machine learning models with the knowledge that some data points will later get correct human labels, even if the model doesn't yet know the labels:

    1 reply 159 proslijeđenih tweetova 622 korisnika označavaju da im se sviđa
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  20. proslijedio/la je Tweet
    10. sij

    Norse exploits the advantages of bio-inspired neural components, that are sparse and event-driven, a fundamental difference from artificial - based on

    14 proslijeđenih tweetova 42 korisnika označavaju da im se sviđa
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