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

Collaborative Diffusion for Multi-Modal Face Generation and Editing

🖥 Github: https://github.com/ziqihuangg/collaborative-diffusion

⏩ Project: https://ziqihuangg.github.io/projects/collaborative-diffusion.html

⏩ Paper: https://arxiv.org/abs/2304.10530v1

⭐️ Dataset: https://paperswithcode.com/dataset/celeba-dialog
@Machine_learn

Book: Applied Machine Learning and High-Performance Computing on AWS
Authors: Mani Khanuja, Farooq Sabir,
Shreyas Subramanian ,Trenton Potgieter
ISBN: 978-1-80323-701-5
year: 2022
pages: 383
Tags: #ML #AWS
@Machine_learn

🔍 Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System

Self-Controlled Memory (SCM) system to unleash infinite-length input capacity for large-scale language models.


🖥 Github: https://github.com/toufunao/SCM4LLMs

⏩ Paper: https://arxiv.org/abs/2304.13343v1

📌 Tasks: https://paperswithcode.com/task/language-modelling

@Machine_learn

ZipIt! Merging Models from Different Tasks without Training

ZipIt allows to combine completely distinct models with different initializations, each solving a separate task, into one multi-task model without any additional training.


🖥 Github: https://github.com/gstoica27/zipit

⏩ Paper: https://arxiv.org/abs/2305.03053v1

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

@Machine_learn

Book: Python for you and me
Release 0.5.beta1
Authors: Kushal Das
ISBN: Null
year: 2023
pages: 175
Tags: #Python #Code
@Machine_learn

Discover and Cure: Concept-aware Mitigation of Spurious Correlation

🖥 Github: https://github.com/wuyxin/disc

⏩ Paper: https://arxiv.org/pdf/2305.00650v1.pdf

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

@Machine_learn

Segment Any Anomaly without Training via Hybrid Prompt Regularization


🖥 Github: https://github.com/caoyunkang/segment-any-anomaly

🖥 Colab: https://colab.research.google.com/drive/1Rwio_KfziuLp79Qh_ugum64Hjnq4ZwsE?usp=sharing

⏩ Paper: https://arxiv.org/abs/2305.11013v1

📌 Dataset: https://paperswithcode.com/dataset/visa
@Machine_learn