Data augmentation for NLP
- nlp
- augmentation
- machine-learning
- artificial-intelligence
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Data augmentation for NLP
An Open Source text-to-speech system built by inverting Whisper.
TranslatedChinese translation notes for Python for Data Analysis, 2nd Edition (2017)
Translated"PyTorch Practical Tutorial" (Second Edition): whether you are starting from zero, working on CV, NLP, or LLM project applications, or moving on to advanced engineering deployment, it's all here. We believe that with the help of this book, readers will be able to master the use of PyTorch with ease and become an excellent deep learning engineer.
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Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Python code for common Machine Learning Algorithms
A better notebook for Scala (and more)
A 15TB Collection of Physics Simulation Datasets
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
[ICCV 2019] Monocular depth estimation from a single image
Performance analysis of predictive (alpha) stock factors
Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
TensorFlow Basic Tutorial Labs
TranslatedWith only a Python foundation, build your own embodied intelligence robot from scratch; step by step from scratch, build VLA/OpenVLA/SmolVLA/Pi0, and gain a deep understanding of embodied intelligence
TranslatedNeeding only a Python foundation, build large language models from scratch; build GLM4\Llama3\RWKV6 step by step from scratch, to gain a deep understanding of how large models work
PyTorch implementation of AnimeGANv2
Segment Anything in Medical Images
Solve puzzles. Improve your pytorch.
Probabilistic reasoning and statistical analysis in TensorFlow
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
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