🔥 ECoDepth: SOTA Diffusive Mono-Depth 🔥

🤨 New SIDE model using a diffusion backbone conditioned on ViT embeddings. It's the new SOTA in SIDE. Source Code released 💙

👉 Review: https://t.ly/s2pbB

👉 Paper: https://lnkd.in/eYt5yr_q

😏 Code: https://lnkd.in/eEcyPQcd

✅ Telegram: https://t.iss.one/DataScienceT

🧑‍🎓 Study of Tensor Network Applications in Complex Networks

📕 Integrated master's thesis in engineering physics

🗓 Publish year: 2022

📎 Study Thesis: https://repositorio.ul.pt/bitstream/10451/57310/1/TM_Francisco_Costa.pdf

✅ Telegram: https://t.iss.one/DataScienceT

🎥 Camera control for text-to-video.

CameraCtrl is a model that provides precise control of the camera position, which allows you to accurately control camera angles and movements when generating a view.

▪ Github: https://github.com/hehao13/CameraCtrl

▪ Paper: https://arxiv.org/abs/2404.02101

▪ Project: https://hehao13.github.io/projects-CameraCtrl/

▪ Weights: https://huggingface.co/hehao13/CameraCtrl/tree/main

✅ Telegram: https://t.iss.one/DataScienceT

🔥 RAG From Scratch 🔥

RAG ( Retrieval Augmented Generation ) is a method of working with LLM, in which the user writes his questions, and the developer programmatically supplements information from external sources and submits everything entirely to the input of the language model. In other words, information is added to the language model in the context of the request, based on which the language model can provide the user with a more complete and accurate answer.

This is a huge list of materials that will help you better understand RAG from the ground up, starting with the basics of indexing, searching and generation. The playlist contains short videos (5-10 minutes) and notebooks with code.

📌 Rag from scratch.
▪ Repository:
https://github.com/langchain-ai/rag-from-scratch
▪ Video playlist:
https://youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x&feature=shared

📌 How RAG can change with long context LLMS.
▪ Video: https://youtube.com/watch?v=SsHUNfhF32s

📌 Adaptive Rag
▪ Video:
https://youtu.be/04ighIjMcAI
▪ Code:
https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_adaptive_rag_cohere.ipynb
▪ Article: https://arxiv.org/abs/2403.14403

📌 Checking the relevance of documents and returning to the search.
▪ Video:
https://youtube.com/watch?v=E2shqsYwxck
▪ Code:
https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_crag.ipynb
▪ Article: https://arxiv.org/pdf/2401.15884.pdf

📌 Bug fixes in RAG:
▪ Code: https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_self_rag.ipynb
Article: https://arxiv.org/abs/2310.11511.pdf

📌 Various approaches to direct questions to the right data source:
▪ Video: https://youtu.be/pfpIndq7Fi8
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_10_and_11.ipynb

📌 Structuring requests
▪ Video: https://youtu.be/kl6NwWYxvbM
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_10_and_11.ipynb
▪ Blog: https://blog.langchain.dev/query-construction/
2/ Deep dive into graphDBs: https://blog.langchain.dev/enhancing-rag-based-applications-accuracy-by-constructing-and-leveraging-knowledge-graphs/
3/ Query structuring: https://python.langchain.com/docs/use_cases/query_analysis/techniques/structuring
4/ Self-search queries: https://python.langchain.com/docs/modules/data_connection/retrievers/self_query

📌 Multi -Representation Indexing
▪ Video: https://youtu.be/gTCU9I6QqCE
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_12_to_14.ipynb
▪ Article: https://arxiv.org/pdf/2312.06648.pdf

📌 Grouping documents by similarity.
▪ Video: https://youtu.be/z_6EeA2LDSw
▪ Code: https://github.com/langchain-ai/langchain/blob/master/cookbook/RAPTOR.ipynb
▪ Article: https://arxiv.org/pdf/2401.18059.pdf

📌 ColBERT
▪ Video: https://youtu.be/cN6S0Ehm7_8
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_12_to_14.ipynb
▪ Article: https://arxiv.org/abs/2004.12832

📌 Query Translation -- Multi Query
▪ Video: https://youtube.com/watch?v=JChPi0CRnDY
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb
▪ Article: https://arxiv.org/pdf/2305.14283.pdf

📌 RAG Fusion
▪ Video: https://youtube.com/watch?v=77qELPbNgxA
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb
▪ Code: https://github.com/Raudaschl/rag-fusion

📌 Query Translation -- Decomposition
▪ Video: https://youtube.com/watch?v=h0OPWlEOank
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb
▪ Articles: https://arxiv.org/pdf/2205.10625.pdf https://arxiv.org/pdf/2212.10509.pdf

📌 Query Translation -- Step Back
▪ Video: https://youtube.com/watch?v=xn1jEjRyJ2U
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb
▪ Article: https://arxiv.org/pdf/2310.06117.pdf

✅ Telegram: https://t.iss.one/DataScienceT

📌 Query Translation -- HyDE
▪ Video:
https://youtube.com/watch?v=SaDzIVkYqyY
▪ Code: https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb
▪ Article: https://arxiv.org/abs/2212.10496

✅ Telegram: https://t.iss.one/DataScienceT

This channels is for Programmers, Coders, Software Engineers.

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

✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ https://t.iss.one/Python53

📁 Data-centric Graph Learning: A Survey

📕 Journal:  JOURNAL OF LATEX CLASS FILES
🗓 Publish year: 2021

🧑‍💻 Authors: Yuxin Guo, Deyu Bo, Cheng Yang, Zhiyuan Lu, Zhongjian Zhang, Jixi Liu, Yufei Peng, Chuan Shi
🏢 Universities:   Beijing University of Posts and Telecommunications

📎 Study the paper

✅ https://t.iss.one/DataScienceT

📁 In silico protein function prediction: the rise of machine learning-based approaches

📕 Journal: Medical Review (De Gruyter)
🗓 Publish year: 2023

🧑‍💻 Authors: Jiaxiao Chen , Zhonghui Gu , Luhua Lai, Jianfeng Pei
🏢 University: Peking University, China

📎 Study the paper

✅ https://t.iss.one/DataScienceT

📃 Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction

📕 Journal: Briefings in Bioinformatics(I.F=13.994)
🗓 Publish year: 2023

🧑‍💻 Authors: Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S Yu, Xiangxiang Zeng
🏢 Universities:  Xiangtan University, Huazhong Agricultural University, Hunan University,

📎 Study the paper