This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

https://www.tg-me.com/DataScienceM
https://www.tg-me.com/DataScienceM

CellViT: Vision Transformers for Precise Cell Segmentation and Classification

🖥 Github: https://github.com/tio-ikim/cellvit

⏩ Paper: https://arxiv.org/pdf/2306.15350v1.pdf

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

@Machine_learn

🖥 RL4CO

🖥 Github: https://github.com/kaist-silab/rl4co

📕 Paper: https://arxiv.org/abs/2306.17100v1

🖥 Colab: https://colab.research.google.com/github/kaist-silab/rl4co/blob/main/notebooks/1-quickstart.ipynb

@Machine_learn

Book: Intro to Python® for Computer Science and Data Science
Authors: Paul Deitel • Harvey Deitel
ISBN: 1-292-36490-4
year: 2022
pages: 882
Tags:#Python #Computer_science #Data_Science
@Machine_learn

Book: Graph Data Modeling in Python
Authors: Gary Hutson, Matt Jackson
ISBN: 978-1-80461-803-5
year: 2023
pages: 236
Tags:#Python #Graph #Data_Science
@Machine_learn

📚 Exploratory Data Analysis with Python Cookbook (2023)

1⃣ Join Channel Download:
https://www.tg-me.com/+MhmkscCzIYQ2MmM8

2⃣ Download Book: https://www.tg-me.com/c/1854405158/105

💬 Tags: #Dataanalysis

USEFUL CHANNELS FOR YOU

🏌️ GlOttal-flow LPC Filter (GOLF)

A DDSP-based neural vocoder.


🖥 Github: https://github.com/yoyololicon/golf

📕 Paper: https://arxiv.org/abs/2306.17252v1

🔗Demo: https://yoyololicon.github.io/golf-demo/

@Machine_learn

The Data Science and Python channel is for researchers and advanced programmers

https://www.tg-me.com/DataScienceT

🧬NeuralFuse

🖥 Github: https://github.com/ibm/neuralfuse

⏩ Paper: https://arxiv.org/pdf/2306.16869v1.pdf

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

@Machine_learn

🤳Filtered-Guided Diffusion

🖥 Github: https://github.com/jaclyngu/filteredguideddiffusion

⏩ Paper: https://arxiv.org/pdf/2306.17141v1.pdf

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

@Machine_learn

​
Recognize Anything: A Strong Image Tagging Model

Get ready for a breakthrough in the realm of AI: introducing the Recognize Anything Model (RAM), a powerful new model that is set to revolutionize image tagging. RAM, a titan in the world of large computer vision models, astoundingly exhibits the zero-shot ability to recognize any common category with an impressive level of accuracy. Shattering traditional approaches, RAM employs a unique paradigm for image tagging, utilizing large-scale image-text pairs for training instead of relying on tedious manual annotations.

Paper link: https://arxiv.org/abs/2306.03514
Code link: https://github.com/xinyu1205/recognize-anything
Project link: https://recognize-anything.github.io/

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-ram

#deeplearning #cv #imagecaptioning
@Machine_lean

ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation ♻️

🖥 Github: https://github.com/pendu/containergym

⏩ Paper: https://arxiv.org/pdf/2307.02991v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/openai-gym

@Machine_learn

Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification

🖥 Github: https://github.com/yuyongcan/benchmark-tta

⏩ Paper: https://arxiv.org/pdf/2307.03133v1.pdf

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

@Machine_learn

Book: MODELING AND SIMULATION
IN PYTHON AN INTRODUSTENNINGERSCIENTISTS
Authors: Allen B. Downey
ISBN: 978-1-7185-0217-8
year: 2023
pages: 344
Tags:#Python #"Modeling
@Machine_learn

با عرض سلام دو پکیچ یادگیری ماشین(یادگیری پایتون، تنسورفلو،کراس) و یادگیری عمیق پیشرفته با تخفیف ۷۰٪ برای دوستان گذاشتیم. جهت خرید می تونین به ایدی بنده پیام بدین.
@Raminmousa

Fourier-Net

Github: https://github.com/xi-jia/fourier-net

Paper: https://arxiv.org/pdf/2307.02997v1.pdf

Dataset: https://paperswithcode.com/dataset/learn2reg

@Machine_learn

🌆Censored Sampling of Diffusion Models Using 3 Minutes of Human Feedback

🖥 Github: https://github.com/tetrzim/diffusion-human-feedback

⏩ Paper: https://arxiv.org/pdf/2307.02770v1.pdf

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

AnimateDiff

Effective framework to animate most of existing personalized text-to-image models once for all, saving the efforts in model-specific tuning.

🖥 Github: https://github.com/guoyww/animatediff/

🖥 Colab: https://colab.research.google.com/github/camenduru/AnimateDiff-colab/blob/main/AnimateDiff_colab.ipynb

📕 Paper: https://arxiv.org/abs/2307.04725

🚀 Project: https://animatediff.github.io/

@Machine_learn

Neural Video Depth Stabilizer (ICCV2023) 🚀🚀🚀

🖥 Github: https://github.com/raymondwang987/nvds

⏩ Paper: https://arxiv.org/pdf/2307.08695v1.pdf

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

@Machine_learn

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://www.tg-me.com/addlist/8_rRW2scgfRhOTc0

✅ Main Channel:
https://www.tg-me.com/DataScienceM