Data Science | Machine Learning with Python for Researchers
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The Data Science and Python channel is for researchers and advanced programmers

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🔥 Llama 2: Open Foundation and Fine-Tuned Chat Models

Llama 2 collection of pretrained and fine-tuned large language models (LLMs).

🖥 Github: https://github.com/facebookresearch/llama

⭐️ Demo: https://huggingface.co/blog/llama2

🤗Hugging face: https://huggingface.co/meta-llama/Llama-2-70b

📕 Paper: https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/

https://t.iss.one/DataScienceT
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SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator

🖥 Github: https://github.com/czvvd/svdformer

Paper: https://arxiv.org/pdf/2307.08492v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/shapenet

https://t.iss.one/DataScienceT
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FLASK: Fine-grained Language Model Evaluation Based on Alignment Skill Sets

🖥 Github: https://github.com/kaistai/flask

Paper: https://arxiv.org/pdf/2307.10928v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/gsm8k

https://t.iss.one/DataScienceT
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🐋 FreeWilly, Large And Mighty Instruction Fine-Tuned Models
.
FreeWilly1 and FreeWilly2 set a new standard in the field of open access Large Language Models.

⭐️ Post: https://stability.ai/blog/freewilly-large-instruction-fine-tuned-models

📌 FreeWilly1: https://huggingface.co/stabilityai/FreeWilly1-Delta-SafeTensor

📌 FreeWilly2: https://huggingface.co/stabilityai/FreeWilly1-Delta-SafeTensor

https://t.iss.one/DataScienceT
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↗️ L-Eval: Instituting Standardized Evaluation for Long Context Language Models

Data and code for L-Eval, a comprehensive long context language models evaluation benchmark.

🖥 Github: https://github.com/bshall/urhythmic

🧑‍💻Model: https://huggingface.co/datasets/L4NLP/LEval

📕 Paper: https://arxiv.org/abs/2307.11088

🚀 Dataset: https://paperswithcode.com/dataset/quality

https://t.iss.one/DataScienceT
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⭐️ CNOS: A Strong Baseline for CAD-based Novel Object Segmentation

Three-stage approach to segment unseen objects in RGB images using their CAD models.

🖥 Github: https://github.com/nv-nguyen/cnos

📕 Paper: https://arxiv.org/abs/2307.11067

🚀 Dataset: https://bop.felk.cvut.cz/datasets/

https://t.iss.one/DataScienceT
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🗣 DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection and Instruction-Aware Models for Conversational AI.

🖥 Github: https://github.com/salesforce/DialogStudio

📕 Paper: https://arxiv.org/abs/2307.10172v2

🔥 Dataset: https://paperswithcode.com/dataset/dialogstudio

https://t.iss.one/DataScienceT
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Kaggle is one of the goto platforms for Machine Learning

Here's how to use it to it's maximum potential 💯

{ Explore Learn Section }
kaggle.com/learn

{ Explore Datasets }
kaggle.com/datasets

{ Explore Challenges}
kaggle.com/competitions

{ Learn By Building }
kaggle.com/code

{ Seek & Give Help }
kaggle.com/discussion

https://t.iss.one/DataScienceT
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Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image Synthesis

🖥 Github: https://github.com/ha0tang/ecgan

📕 Paper: https://arxiv.org/abs/2307.12084v1

🔥 Dataset: https://paperswithcode.com/dataset/cityscapes

https://t.iss.one/DataScienceT
Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG

🖥 Github: https://github.com/remotebiosensing/rppg

📕 Paper: https://arxiv.org/abs/2307.12644v1

🔥 Dataset: https://paperswithcode.com/dataset/ubfc-rppg

https://t.iss.one/DataScienceT
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TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition (ICCV 2023)

🖥 Github: https://github.com/Shilin-LU/TF-ICON

📕 Paper: https://arxiv.org/abs/2307.12644v1

🔥 Dataset: https://paperswithcode.com/dataset/ubfc-rppg

https://t.iss.one/DataScienceT
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