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

✅ Data Science Channels:
https://t.iss.one/addlist/8_rRW2scgfRhOTc0

✅ Main Channel:
https://t.iss.one/DataScienceM

🔥 DEGramNet: A Novel Convolutional Architecture for Audio Analysis 🚀

📄 Paper: https://link.springer.com/article/10.1007/s00521-023-08849-7
🔥 PyTorch code: https://github.com/robertanto/DEGramNet-torch
📦 TensorFlow code: https://github.com/MiviaLab/DEGramNet
🔗 Google Colab: https://link.springer.com/article/10.1007/s00521-023-08849-7

@Machine_learn

FATRER

🖥 Github: https://github.com/ludybupt/FATRER

📕 Paper: https://arxiv.org/pdf/2307.12221v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/iemocap

@Machine_learn

Revisiting the Minimalist Approach to Offline Reinforcement Learning

🖥 Github: https://github.com/tinkoff-ai/rebrac

📕 Paper: https://arxiv.org/pdf/2305.09836v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/d4rl

@Machine_learn

Book: Blockchain Tethered AI
Trackable, Traceable Artificial Intelligence and Machine Learning
Authors: Karen Kilroy, Lynn Riley, and Deepak Bhatta
ISBN: 978-1-098-13048-0
year: 2023
pages: 307
Tags:#Python #Blockchain
@Machine_learn

🚀 AgentBench: Evaluating LLMs as Agents.

AgentBench, a multi-dimensional evolving benchmark that currently consists of 8 distinct environments to assess LLM-as-Agent's reasoning and decision-making abilities in a multi-turn open-ended generation setting.


🖥 Github: https://github.com/thudm/agentbench

📕 Paper: https://arxiv.org/abs/2308.03688v1

☑️ Dataset: https://paperswithcode.com/dataset/alfworld
@Machine_learn

🦙 LLM Attacks

Universal and Transferable Attacks on Aligned Language Models.

🖥 Github: https://github.com/llm-attacks/llm-attacks

📕 Paper: https://arxiv.org/abs/2307.15043v1

🔗 Dataset: https://paperswithcode.com/dataset/ethics-1

@Machine_learn

⏩ SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

A benchmark for evaluating Multimodal LLMs using multiple-choice questions.



🖥 Github: https://github.com/ailab-cvc/seed-bench

📕 Paper: https://arxiv.org/abs/2307.16125v1

☑️ Dataset: https://paperswithcode.com/dataset/seed-bench

@Machine_learn

Book: Git Repository
Management in 30 Days
Authors: Sumit Jaiswal
ISBN: 978-93-55518-071
year: 2023
pages: 290
Tags:#GIT
@Machine_learn

Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition

🖥 Github: https://github.com/osvai/ske2grid

📕 Paper: https://arxiv.org/pdf/2308.07571v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/ucf101

@Machin_learn

تخفيف ويژه دو پكيچ يادگيري عميق ٤٥ جلسه اي و ياديگيري عميق با ٣٦ پروژه عملي براي دوستاني كه نياز دارند.

@Raminmousa

Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

🖥 Github: https://github.com/llvy21/duic

📕 Paper: https://arxiv.org/pdf/2308.07733v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/pixel-art

@Machine_learn

S3A: Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

🖥 Github: https://github.com/sheng-eatamath/s3a

📕 Paper: https://arxiv.org/pdf/2308.12960v1.pdf

🔥 Dataset: https://paperswithcode.com/dataset/cifar-100

@Machine_learn

این تخفیف تا اخر شب پا برجاست

🐕 Reprogramming Under Constraints

🖥 Github: https://github.com/landskape-ai/reprogram_lt

📕 Paper: https://arxiv.org/pdf/2308.14969v1.pdf

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

@Machine_learn

⭐️ScrollNet: Dynamic Weight Importance for Continual Learning

git clone https://github.com/FireFYF/ScrollNet.git
cd ScrollNet

🖥 Github: https://github.com/firefyf/scrollnet

📕 Paper: https://arxiv.org/abs/2308.16567v1

🔥 Dataset: https://paperswithcode.com/dataset/tiny-imagenet

@Machine_learn

⚡️ Improving Pixel-based MIM by Reducing Wasted Modeling Capability

A new method that explicitly utilizes low-level features from shallow layers to aid pixel reconstruction.



🖥 Github: https://github.com/open-mmlab/mmpretrain

📕 Paper: https://arxiv.org/abs/2308.00261v1

⭐️Project: mmpretrain.readthedocs.io/en/latest/

☑️ Dataset: https://paperswithcode.com/dataset/coco

@Machine_learn