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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Cheat Sheets for AI Neural Networks, Machine Learning, DeepLearning & Big Data

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Are you looking to break into Machine Learning?

Here is a great place to start:

This is the best video series about Neural Networks for anyone who wants to understand how they work.

https://www.3blue1brown.com/topics/neural-networks

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Get up to speed on Large Language Models with these two free cheat sheets

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Forwarded from Eng. Hussein Sheikho 👨‍💻
This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages

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🔥 Artificial Intelligence for Science (AIRS)

OpenQM: AI for Quantum Mechanics
OpenDFT: AI for Density Functional Theory
OpenMol: AI for Small Molecules
OpenProt: AI for Protein Science
OpenMat: AI for Materials Science
OpenMI: AI for Molecular Interactions
OpenPDE: AI for Partial Differential Equations

🖥 Github: https://github.com/divelab/AIRS

📕 Paper: https://arxiv.org/abs/2307.08423

⭐️ Website: https://www.air4.science/

📌 Dataset: https://paperswithcode.com/dataset/atom3d

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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/

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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

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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

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