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🐼 PandaLM: ReProducible and Automated Language Model Assessment

Judge large language model, named PandaLM, which is trained to distinguish the superior model given several LLMs. PandaLM's focus extends beyond just the objective correctness of responses, which is the main focus of traditional evaluation datasets.

🖥 Github: https://github.com/weopenml/pandalm

📕 Paper: https://arxiv.org/abs/2306.05087v1

🔗 Dataset: https://github.com/tatsu-lab/stanford_alpaca#data-release

https://t.iss.one/DataScienceT

📹 Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

LLaMA is working on empowering large language models with video and audio understanding capability.

🖥 Github: https://github.com/damo-nlp-sg/video-llama

📕 Paper: https://arxiv.org/abs/2306.02858

⏩ Demo: https://huggingface.co/spaces/DAMO-NLP-SG/Video-LLaMA

📌 Model: https://modelscope.cn/studios/damo/video-llama/summary

https://t.iss.one/DataScienceT

A list of the best Telegram channels related to data science, programming languages, and artificial intelligence.

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🏔️ Large Language Model for Geoscience

We introduce K2 (7B), an open-source language model trained by firstly further pretraining LLaMA on collected and cleaned geoscience literature, including geoscience open-access papers and Wikipedia pages, and secondly fine-tuning with knowledge-intensive instruction tuning data (GeoSignal).

git clone https://github.com/davendw49/k2.git
cd k2
conda env create -f k2.yml
conda activate k2

🖥 Github: https://github.com/davendw49/k2

⭐️ Demo: https://huggingface.co/daven3/k2_fp_delta

📕 Paper: https://arxiv.org/abs/2306.05064v1

🔗 Dataset: https://huggingface.co/datasets/daven3/geosignal

https://t.iss.one/DataScienceT

💲 FinGPT: Open-Source Financial Large Language Models

Unlike proprietary models, FinGPT takes a data-centric approach, providing researchers and practitioners with accessible and transparent resources to develop their FinLLMs.

🖥 Github: https://github.com/ai4finance-foundation/fingpt

⭐️ FinNLP: https://github.com/ai4finance-foundation/finnlp

📕 Paper: https://arxiv.org/abs/2306.06031v1

🔗 Project: https://ai4finance-foundation.github.io/FinNLP/

https://t.iss.one/DataScienceT

You can now download and watch all paid data science courses for free by subscribing to our new channel

https://t.iss.one/udemy13

GP-UNIT - Official PyTorch Implementation

🖥 Github: https://github.com/williamyang1991/gp-unit

⏩ Paper: https://arxiv.org/pdf/2306.04636v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/celeba-hq

https://t.iss.one/DataScienceT

🧔 4DHumans: Reconstructing and Tracking Humans with Transformers

Fully "transformerized" version of a network for human mesh recovery.

🖥 Github: https://github.com/shubham-goel/4D-Humans

⭐️ Colab: https://colab.research.google.com/drive/1Ex4gE5v1bPR3evfhtG7sDHxQGsWwNwby?usp=sharing

📕 Paper: https://arxiv.org/pdf/2305.20091.pdf

🔗 Project: https://shubham-goel.github.io/4dhumans/

https://t.iss.one/DataScienceT

A channel for educational Python courses

All courses here are free and available to everyone

https://t.iss.one/Python53

⚡ 21 Must-Have Cheat Sheets for Data Science Interviews: Unlocking Your Path to Success

▪SQL

1. SQL Basics Cheat Sheet
2. The Essential SQL Commands Cheat Sheet for Beginners
3. SQL Cheat Sheet – Technical Concepts for the Job Interview

▪Python

4. Python Cheat Sheet
5. Python Cheat Sheet
6. Comprehensive Python Cheatsheet

▪R

7. RStudio Cheatsheets

▪Data Structures

8. Data Structures Reference
9. An Executable Data Structures Cheat Sheet for Interviews

▪Data Manipulation

10. Pandas Cheat Sheet for Data Science
11. Pandas Cheat Sheet
12. Data Wrangling With pandas Cheat Sheet

▪Data Visualization

13. Data Visualization Cheat Sheet
14. Data Visualization Cheat Sheet
15. Data Visualization Cheat Sheets

▪Statistics & Probability

16. A Comprehensive Statistics Cheat Sheet for Data Science Interviews
17. The Most Comprehensive Stats Cheat Sheet
18. Statistics Cheat Sheet

▪Algorithms & Models

19. Top Prediction Algorithms
20. Your Ultimate Data Science Statistics & Mathematics Cheat Sheet
21. Cheat Sheet for Machine Learning Models

https://t.iss.one/DataScienceT

Galactic: Scaling End-to-End Reinforcement Learning for Rearrangement
at 100k Steps-Per-Second

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

⏩ Paper: https://arxiv.org/pdf/2306.07552v1.pdf

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

https://t.iss.one/DataScienceT

Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]

🖥 Github: https://github.com/pietroastolfi/suave-daino

⏩ Paper: https://arxiv.org/pdf/2306.07483v1.pdf

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

https://t.iss.one/DataScienceT

🌐 WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings

🖥 Github: https://github.com/poloclub/wizmap

⭐️ Colab: https://colab.research.google.com/drive/1GNdmBnc5UA7OYBZPtHu244eiAN-0IMZA?usp=sharing

📕 Paper: https://arxiv.org/abs/2306.09328v1

🔗 Web demo: https://poloclub.github.io/wizmap.

https://t.iss.one/DataScienceT

How do Transformers work?

All the Transformer models mentioned above (GPT, BERT, BART, T5, etc.) have been trained as language models. This means they have been trained on large amounts of raw text in a self-supervised fashion. Self-supervised learning is a type of training in which the objective is automatically computed from the inputs of the model. That means that humans are not needed to label the data!

This type of model develops a statistical understanding of the language it has been trained on, but it’s not very useful for specific practical tasks. Because of this, the general pretrained model then goes through a process called transfer learning. During this process, the model is fine-tuned in a supervised way — that is, using human-annotated labels — on a given task

🔗 Read More

🌸 https://t.iss.one/DataScienceT