Deep-Learning-Experiments
python_autocomplete
| Deep-Learning-Experiments | python_autocomplete | |
|---|---|---|
| 1 | 8 | |
| 1,081 | 182 | |
| - | 1.1% | |
| 8.3 | 0.0 | |
| about 1 month ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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Deep-Learning-Experiments
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EEE 197 - Deep Learning
Hello, took the course last sem. Maraming napa-drop sa amin dahil sa difficulty nung assignments pero doable naman. Open-source mismo yung course, available sya sa GitHub: https://github.com/roatienza/Deep-Learning-Experiments
python_autocomplete
- Show HN: Transformer XL model for fast Python auto-completion
- Show HN: Python Autocompletion with a Transformer XL
- Simple VSCode extension to autocomple Python with a transformer model
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Show HN: Simple VSCode extension to autocomplete Python with a transformer model
Github repo: https://github.com/lab-ml/python_autocomplete
- lab-ml/python_autocomplete Python Autocomplete This project try autocompleting python source code using LSTM or Transformer models.
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Show HN: Autocomplete Python Code with Transformers
Links:
Github repo: https://github.com/lab-ml/python_autocomplete
Training notebook: https://colab.research.google.com/github/lab-ml/python_autoc...
Evaluation notebook: https://colab.research.google.com/github/lab-ml/python_autoc...
What are some alternatives?
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
DeepLearning - Contains all my works, references for deep learning
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
pytorch-deepdream - PyTorch implementation of DeepDream algorithm (Mordvintsev et al.). Additionally I've included playground.py to help you better understand basic concepts behind the algo.
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
sudo_rm_rf - Code for SuDoRm-Rf networks for efficient audio source separation. SuDoRm-Rf stands for SUccessive DOwnsampling and Resampling of Multi-Resolution Features which enables a more efficient way of separating sources from mixtures.