🔎 DeepOnto: A Python Package for Ontology Engineering with Deep Learning
A package for ontology engineering with deep learning and language model.
pip install deeponto
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
A package for ontology engineering with deep learning and language model.
pip install deeponto
🖥 Github: https://github.com/KRR-Oxford/DeepOnto
📌 Project: https://krr-oxford.github.io/DeepOnto/
📕 Paper: https://arxiv.org/abs/2307.03067v1
🚀 Dataset: https://paperswithcode.com/dataset/ontolama
https://t.iss.one/DataScienceT
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Top 6 Algorithms Every Software Engineer Should Know
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
1) Binary Search Algorithm.
2) Bubble Sort Algorithm.
3) Merge Sort Algorithm
4) Depth-first Search Algorithm
5) Dijkstra’s Algorithm
6) Randomized Algorithm
https://t.iss.one/DataScienceT
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⭐️ InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval.
🖥 Github: https://github.com/zetaalphavector/inpars
📕 Paper: https://arxiv.org/abs/2307.04601v1
🚀 Dataset: https://paperswithcode.com/dataset/beir
https://t.iss.one/DataScienceT
pip install inpars🖥 Github: https://github.com/zetaalphavector/inpars
📕 Paper: https://arxiv.org/abs/2307.04601v1
🚀 Dataset: https://paperswithcode.com/dataset/beir
https://t.iss.one/DataScienceT
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Django Roadmap
Link 1: https://github.com/HHHMHA/django-roadmap
Link 2:
https://github.com/faresemad/Django-Roadmap
Share this roadmap for your friends
https://t.iss.one/CodeProgrammer
Link 1: https://github.com/HHHMHA/django-roadmap
Link 2:
https://github.com/faresemad/Django-Roadmap
Share this roadmap for your friends
https://t.iss.one/CodeProgrammer
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Fourier-Net
🖥 Github: https://github.com/xi-jia/fourier-net
⏩ Paper: https://arxiv.org/pdf/2307.02997v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/learn2reg
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/xi-jia/fourier-net
⏩ Paper: https://arxiv.org/pdf/2307.02997v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/learn2reg
https://t.iss.one/DataScienceT
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🔥 Generative Pretraining in Multimodality
Model can take in any single-modality or multimodal data input indiscriminately through a one-model-for-all autoregressive training process.
🖥 Github: https://github.com/baaivision/emu
📕 Paper: https://arxiv.org/abs/2307.05222v1
🚀 Dataset: https://paperswithcode.com/dataset/mmc4
https://t.iss.one/DataScienceT
Model can take in any single-modality or multimodal data input indiscriminately through a one-model-for-all autoregressive training process.
🖥 Github: https://github.com/baaivision/emu
📕 Paper: https://arxiv.org/abs/2307.05222v1
🚀 Dataset: https://paperswithcode.com/dataset/mmc4
https://t.iss.one/DataScienceT
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AnimateDiff
Effective framework to animate most of existing personalized text-to-image models once for all, saving the efforts in model-specific tuning.
🖥 Github: https://github.com/guoyww/animatediff/
🖥 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb
📕 Paper: https://arxiv.org/abs/2307.04725
🚀 Project: https://animatediff.github.io/
https://t.iss.one/DataScienceT
Effective framework to animate most of existing personalized text-to-image models once for all, saving the efforts in model-specific tuning.
🖥 Github: https://github.com/guoyww/animatediff/
🖥 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb
📕 Paper: https://arxiv.org/abs/2307.04725
🚀 Project: https://animatediff.github.io/
https://t.iss.one/DataScienceT
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machinelearningAIDeep_resume.pdf
45.4 MB
Cheat Sheets for AI Neural Networks, Machine Learning, DeepLearning & Big Data
💐 Please React ♥️, Share
https://t.iss.one/DataScienceM
💐 Please React ♥️, Share
https://t.iss.one/DataScienceM
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🔈 Urhythmic: Rhythm Modeling for Voice Conversion
Unsupervised Rhythm Modeling for Voice Conversion.
🖥 Github: https://github.com/bshall/urhythmic
🖥 Documentation: https://colab.research.google.com/github/bshall/urhythmic/blob/main/urhythmic_demo.ipynb
📕 Paper: https://arxiv.org/abs/2307.06040v1
🚀 Dataset: https://paperswithcode.com/dataset/vctk
https://t.iss.one/DataScienceT
Unsupervised Rhythm Modeling for Voice Conversion.
🖥 Github: https://github.com/bshall/urhythmic
🖥 Documentation: https://colab.research.google.com/github/bshall/urhythmic/blob/main/urhythmic_demo.ipynb
📕 Paper: https://arxiv.org/abs/2307.06040v1
🚀 Dataset: https://paperswithcode.com/dataset/vctk
https://t.iss.one/DataScienceT
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