Book: DATA MINING
FOR BUSINESS ANALYTICS(Concepts, Techniques, and Applications in Python)
Authors: GALIT SHMUELI, PETER C., BRUCE PETER, and GEDECK NITIN R. PATEL
ISBN: Null
year: 2019
pages: 681
Tags: #Python #datamining #business
@Machine_learn

Slide: Do large language models need sensory grounding for meaning and understanding
Supervised learning (SL)
Reinforcement learning (RL)
Self-Supervised Learning (SSL)
year:2023
pages:38
tags: #DL #ML #SL #RL #SSL
@Machine_learn

⭐️Title: HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

🖥 Github: https://github.com/microsoft/JARVIS

⏩ Paper: https://arxiv.org/abs/2303.17604v1

@Machine_learn

Book: Designing Machine Systems An Iterative Process for Production-Ready Applications
Authors: Chip Huyen
ISBN: 978-1-098-10796-3
year: 2022
pages: 463
Tags: #Python #datamining #ML
@Machine_learn

WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research

Propose a three-stage processing pipeline for filtering noisy data and generating high-quality captions, where ChatGPT.
🖥 Github: https://github.com/xinhaomei/wavcaps

⏩ Paper: https://arxiv.org/abs/2303.17395v1

🧱Dataset: https://paperswithcode.com/dataset/sounddescs

@Machine_learn

Book: Algorithms for Decision Making
Authors: Mykel J. Kochenderfer, Tim A.Wheeler, and Kyle H. Wray
ISBN: Null
year: 2022
pages: 690
Tags: #Decision_Making #NN #LR
@Machine_learn

Title: TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data
Arxiv link: https://arxiv.org/abs/2304.02094
Authors: Faraz Sasani, @RaminMousa, Ali Karkehabadi, Samin Dehbashi, Ali Mohammadi
doi: https://doi.org/10.48550/arXiv.2304.02094

@Machine_learn

Book: Pandas for Everyone
Authors: D A N I E L Y. C H E N
ISBN: 978-0-13-789115-3
year: 2023
pages: 1148
Tags: #Pandas #python
@Machine_learn

Instruction Tuning with GPT-4

First attempt to use GPT-4 to generate instruction-following data for LLM finetuning.

🖥 Github: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM

⏩ Paper: https://arxiv.org/abs/2304.03277v1

⏩ Project: https://instruction-tuning-with-gpt-4.github.io/

@Machine_learn

با عرض سلام موضوعاتی‌که برای‌مقالات به دوستان پیشنهاد میکنم رو در ادامه اوردم .
#کریپتو_کارنسی
۱- ارائه رویکرد ترکیبی برای پیش بینی عدم نقدشوندگی‌کریپتوکارنسی(رگرسیونی/طبقه بندی)
۲-استفاده از اطلاعات تحلیل‌احساس و اطلاعات هش ریت در پیش بینی قیمت کریپتو کارنسی
#ترافیک_شبکه
۳-استفاده از یادگیری فدرالی برای پیش بینی جریان بسته
۴-استفاده از رویکردهای همگن و غیر همگن fusion برای طبقه بندی ترافیک شبکه
۵- استفاده از رویکردهای complex valued neural net‌برای پیش بینی جریان بسته
#تصویر_متن
در کل میشه بالا ۱۰۰ پروژه در این حوزه تعریف کرد و بسته به دیتاهای متفاوت میشه از رویکردهای مبتنی بر fusion, transformer, capsule,.... استفاده کرد.

سوالیم داشتین در خدمتم

@Raminmousa

⚜️ OpenAGI: When LLM Meets Domain Experts

Reinforcement Learning from Task Feedback (RLTF) mechanism, which uses the task-solving result as feedback to improve the LLM's task-solving ability


!git clone https://github.com/agiresearch/OpenAGI.git

🖥 Github: https://github.com/agiresearch/openagi

⏩ Paper: https://arxiv.org/pdf/2304.04370.pdf

⭐️ Dataset: https://drive.google.com/drive/folders/1AjT6y7qLIMxcmHhUBG5IE1_5SnCPR57e?usp=share_link

@Machine_learn

Book: Applied Recommender Systems with Python
Authors: Akshay Kulkarni Adarsha ,Shivananda Anoosh Kulkarni
V, Adithya Krishnan
ISBN: 978-1-4842-8954-9
year: 2022
pages: 257
Tags: #Recommender_system #python
@Machine_learn

عيدكم مبارك
@Machine_learn

Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment

🖥 Github: https://github.com/k-zha14/zoom-vqa

⏩ Paper: https://arxiv.org/pdf/2304.06440v1.pdf

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

@Machine_learn

LLM Zoo: democratizing ChatGPT

Model "Phoenix", achieving competitive performance among open-source English and Chinese models while excelling in languages with limited resources

🖥 Github: https://github.com/freedomintelligence/llmzoo

⏩ Paper: https://arxiv.org/abs/2304.10453v1

⭐️ Parameters: https://huggingface.co/FreedomIntelligence/phoenix-chat-7b

@Machine_learn

Transfer Knowledge from Head to Tail: Uncertainty Calibration under Long-tailed Distribution

🖥 Github: https://github.com/jiahaochen1/calibration

⏩ Paper: https://arxiv.org/pdf/2304.06537v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/cifar-10

@Machine_learn

Count anything

An empirical study on few-shot counting using segment anything

🖥 Github: https://github.com/vision-intelligence-and-robots-group/count-anything

⏩ Paper: https://arxiv.org/abs/2304.10817v1

🤗 Hugging face: https://huggingface.co/spaces/nebula/counting-anything

📌 Dataset: https://drive.google.com/file/d/1ymDYrGs9DSRicfZbSCDiOu0ikGDh5k6S/view?usp=sharing

@Machine_learn

Improving Segmentation of Objects with Varying Sizes in Biomedical Images using Instance-wise and Center-of-Instance Segmentation Loss Function

🖥 Github: https://github.com/brainimageanalysis/ici-loss

⏩ Paper: https://arxiv.org/pdf/2304.06229v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/atlas-v2-0

@Machine_learn