🔥 Here's a list of 32 datasets that you can go over the weekend:
https://datasciencedojo.com/blog/datasets-data-science-skills/
✅ More reaction = more projects
@CodeProgrammer ♥️
https://datasciencedojo.com/blog/datasets-data-science-skills/
✅ More reaction = more projects
@CodeProgrammer ♥️
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How to Encrypt and Decrypt Image Using Python | How to Encrypt any Image File Using Python
https://morioh.com/p/978e38a1f65b?f=5c21fb01c16e2556b555ab32
✅ More reaction = more projects
@CodeProgrammer ♥️
https://morioh.com/p/978e38a1f65b?f=5c21fb01c16e2556b555ab32
✅ More reaction = more projects
@CodeProgrammer ♥️
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DragGAN.gif
20.6 MB
Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
Paper:
https://arxiv.org/abs/2305.10973
Github:
https://github.com/XingangPan/DragGAN
Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/
https://t.iss.one/DataScienceT
Paper:
https://arxiv.org/abs/2305.10973
Github:
https://github.com/XingangPan/DragGAN
Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/
https://t.iss.one/DataScienceT
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🦙 LLM-Pruner: On the Structural Pruning of Large Language Models
Compress your LLMs to any size;
🖥 Github: https://github.com/horseee/llm-pruner
⏩ Paper: https://arxiv.org/abs/2305.11627v1
📌 Dataset: https://paperswithcode.com/dataset/piqa
https://t.iss.one/DataScienceT
Compress your LLMs to any size;
🖥 Github: https://github.com/horseee/llm-pruner
⏩ Paper: https://arxiv.org/abs/2305.11627v1
📌 Dataset: https://paperswithcode.com/dataset/piqa
https://t.iss.one/DataScienceT
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Mask-Free Video Instance Segmentation
MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state.
🖥 Github: https://github.com/SysCV/maskfreevis
⏩ Paper: https://arxiv.org/pdf/2303.15904.pdf
📌 Project: https://www.vis.xyz/pub/maskfreevis/
https://t.iss.one/DataScienceT
MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state.
🖥 Github: https://github.com/SysCV/maskfreevis
⏩ Paper: https://arxiv.org/pdf/2303.15904.pdf
📌 Project: https://www.vis.xyz/pub/maskfreevis/
https://t.iss.one/DataScienceT
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📎 Instruction-tuning Stable Diffusion with InstructPix2Pix
InstructPix2Pix training strategy to follow more specific instructions related to tasks in image translation (such as cartoonization) and low-level image processing (such as image deraining).
🖥 Post: https://huggingface.co/blog/instruction-tuning-sd
⭐️ Training and inference code: https://github.com/huggingface/instruction-tuned-sd
📌 Demo: https://huggingface.co/spaces/instruction-tuning-sd/instruction-tuned-sd
⏩ InstructPix2Pix: https://huggingface.co/timbrooks/instruct-pix2pix
🔍Datasets and models from this post: https://huggingface.co/instruction-tuning-sd
https://t.iss.one/DataScienceT
InstructPix2Pix training strategy to follow more specific instructions related to tasks in image translation (such as cartoonization) and low-level image processing (such as image deraining).
🖥 Post: https://huggingface.co/blog/instruction-tuning-sd
⭐️ Training and inference code: https://github.com/huggingface/instruction-tuned-sd
📌 Demo: https://huggingface.co/spaces/instruction-tuning-sd/instruction-tuned-sd
⏩ InstructPix2Pix: https://huggingface.co/timbrooks/instruct-pix2pix
🔍Datasets and models from this post: https://huggingface.co/instruction-tuning-sd
https://t.iss.one/DataScienceT
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QLoRA: Efficient Finetuning of Quantized LLMs
Model name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
🖥 Github: https://github.com/artidoro/qlora
⏩ Paper: https://arxiv.org/abs/2305.14314
⭐️ Demo: https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi
📌 Dataset: https://paperswithcode.com/dataset/ffhq
https://t.iss.one/DataScienceT
Model name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
🖥 Github: https://github.com/artidoro/qlora
⏩ Paper: https://arxiv.org/abs/2305.14314
⭐️ Demo: https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi
📌 Dataset: https://paperswithcode.com/dataset/ffhq
https://t.iss.one/DataScienceT
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Large Language Models as Tool Makers
In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.
🖥 Github: https://github.com/ctlllll/llm-toolmaker
⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf
📌 Dataset: https://paperswithcode.com/dataset/big-bench
https://t.iss.one/DataScienceT
In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.
🖥 Github: https://github.com/ctlllll/llm-toolmaker
⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf
📌 Dataset: https://paperswithcode.com/dataset/big-bench
https://t.iss.one/DataScienceT
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Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models
The performance of Text2Image is largely dependent on text prompts. In Prompt-Free Diffusion, no prompt is needed, just a reference images.
🖥 Github: https://github.com/shi-labs/prompt-free-diffusion
🔎 Demo: https://huggingface.co/spaces/shi-labs/Prompt-Free-Diffusion
⏩ Paper: https://arxiv.org/abs/2305.16223v1
📌 Dataset: https://paperswithcode.com/dataset/ffhq
https://t.iss.one/DataScienceT
The performance of Text2Image is largely dependent on text prompts. In Prompt-Free Diffusion, no prompt is needed, just a reference images.
🖥 Github: https://github.com/shi-labs/prompt-free-diffusion
🔎 Demo: https://huggingface.co/spaces/shi-labs/Prompt-Free-Diffusion
⏩ Paper: https://arxiv.org/abs/2305.16223v1
📌 Dataset: https://paperswithcode.com/dataset/ffhq
https://t.iss.one/DataScienceT
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Large Language Models as Tool Makers
In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.
🖥 Github: https://github.com/ctlllll/llm-toolmaker
⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf
📌 Dataset: https://paperswithcode.com/dataset/big-bench
https://t.iss.one/DataScienceT
In this work, we take an initial step towards removing this dependency by proposing a closed-loop framework, referred to as LLMs A s Tool Makers (LATM), where LLMs create their own reusable tools for problem-solving.
🖥 Github: https://github.com/ctlllll/llm-toolmaker
⏩ Paper: https://arxiv.org/pdf/2305.17126v1.pdf
📌 Dataset: https://paperswithcode.com/dataset/big-bench
https://t.iss.one/DataScienceT
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🖥 A Practical Toolkit for Multilingual Question and Answer Generation
Multilingual/multidomain question generation datasets, models, and python library for question generation.
🖥 Github: https://github.com/asahi417/lm-question-generation
⏩ Paper: https://arxiv.org/abs/2305.17416v1
📌 Dataset: https://paperswithcode.com/dataset/squad
https://t.iss.one/DataScienceT
Multilingual/multidomain question generation datasets, models, and python library for question generation.
🖥 Github: https://github.com/asahi417/lm-question-generation
⏩ Paper: https://arxiv.org/abs/2305.17416v1
📌 Dataset: https://paperswithcode.com/dataset/squad
https://t.iss.one/DataScienceT
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🦙 BigTrans 🚀
BigTrans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languag
🖥 Github: https://github.com/ZNLP/BigTrans/tree/main
⏩ Paper: https://arxiv.org/abs/2305.18098v1
📌 Dataset: https://paperswithcode.com/dataset/flores-200
https://t.iss.one/DataScienceT
BigTrans which adapts LLaMA that covers only 20 languages and enhances it with multilingual translation capability on more than 100 languag
🖥 Github: https://github.com/ZNLP/BigTrans/tree/main
⏩ Paper: https://arxiv.org/abs/2305.18098v1
📌 Dataset: https://paperswithcode.com/dataset/flores-200
https://t.iss.one/DataScienceT
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🔥 GPT4Tools: Teaching LLM to Use Tools via Self-instruction
GPT4Tools is a centralized system that can control multiple visual foundation models. It is based on Vicuna (LLaMA), and 71K self-built instruction data.
🖥 Github: https://github.com/stevengrove/gpt4tools
⏩ Paper: https://arxiv.org/abs/2305.18752v1
📌 Project: https://gpt4tools.github.io/
https://t.iss.one/DataScienceT
GPT4Tools is a centralized system that can control multiple visual foundation models. It is based on Vicuna (LLaMA), and 71K self-built instruction data.
🖥 Github: https://github.com/stevengrove/gpt4tools
⏩ Paper: https://arxiv.org/abs/2305.18752v1
📌 Project: https://gpt4tools.github.io/
https://t.iss.one/DataScienceT
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Introducing BERTopic Integration with the Hugging Face Hub
BERTopic provides a powerful tool for users to uncover significant topics within text collections, thereby gaining valuable insights.
pip install bertopic
🤗 Hugging face: https://huggingface.co/blog/bertopic
🖥 Github: https://github.com/MaartenGr/BERTopic
⏩ Colab: https://colab.research.google.com/#fileId=https://huggingface.co/spaces/davanstrien/blog_notebooks/blob/main/BERTopic_hub_starter.ipynb
📌 Docs: https://maartengr.github.io/BERTopic/getting_started/quickstart/quickstart.html
https://t.iss.one/DataScienceT
BERTopic provides a powerful tool for users to uncover significant topics within text collections, thereby gaining valuable insights.
pip install bertopic
🤗 Hugging face: https://huggingface.co/blog/bertopic
🖥 Github: https://github.com/MaartenGr/BERTopic
⏩ Colab: https://colab.research.google.com/#fileId=https://huggingface.co/spaces/davanstrien/blog_notebooks/blob/main/BERTopic_hub_starter.ipynb
📌 Docs: https://maartengr.github.io/BERTopic/getting_started/quickstart/quickstart.html
https://t.iss.one/DataScienceT
Dynamic Sparse Training with Structured Sparsity
🖥 Github: https://github.com/calgaryml/condensed-sparsity
⏩ Paper: https://arxiv.org/pdf/2305.02299v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/cifar-10
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/calgaryml/condensed-sparsity
⏩ Paper: https://arxiv.org/pdf/2305.02299v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/cifar-10
https://t.iss.one/DataScienceT
SSSegmenation
🖥 Github: https://github.com/segmentationblwx/sssegmentation
⏩ Paper: https://arxiv.org/pdf/2305.17091v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/cityscapes
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/segmentationblwx/sssegmentation
⏩ Paper: https://arxiv.org/pdf/2305.17091v1.pdf
💨 Dataset: https://paperswithcode.com/dataset/cityscapes
https://t.iss.one/DataScienceT
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🔥 10 Free Machine Learning Courses from Top Universities
1. Introduction to Machine Learning - UC Berkeley
2. Introduction to Machine Learning - Carnegie Mellon University
3. Machine Learning - Stanford University
4. Machine Learning & Data Mining - Caltech
5. Learning from Data - Caltech
6. Machine Learning for Intelligent Systems - Cornell University
7. Large Scale Machine Learning - University of Toronto
8. Machine Learning with Large Datasets - Carnegie Mellon University
9. Foundations of Machine Learning and Statistical Inference - Caltech
10. Algorithmic Aspects of Machine Learning - MIT
https://t.iss.one/DataScienceT
1. Introduction to Machine Learning - UC Berkeley
2. Introduction to Machine Learning - Carnegie Mellon University
3. Machine Learning - Stanford University
4. Machine Learning & Data Mining - Caltech
5. Learning from Data - Caltech
6. Machine Learning for Intelligent Systems - Cornell University
7. Large Scale Machine Learning - University of Toronto
8. Machine Learning with Large Datasets - Carnegie Mellon University
9. Foundations of Machine Learning and Statistical Inference - Caltech
10. Algorithmic Aspects of Machine Learning - MIT
https://t.iss.one/DataScienceT
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