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

https://t.iss.one/DataScienceT

LibCity

🖥 Github: https://github.com/libcity/bigscity-libcity

📕 Paper: https://arxiv.org/pdf/2308.12899v1.pdf

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

https://t.iss.one/DataScienceT

Surprise 2
https://t.iss.one/+MhmkscCzIYQ2MmM8

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

https://t.iss.one/DataScienceT

🖥 Roadmap of free courses for learning Python and Machine learning.

▪Data Science
▪ AI/ML
▪ Web Dev

1. Start with this
https://kaggle.com/learn/python

2. Take any one of these

❯ https://openclassrooms.com/courses/6900856-learn-programming-with-python

❯ https://scaler.com/topics/course/python-for-beginners/

❯ https://simplilearn.com/learn-python-basics-free-course-skillup

3. Then take this
https://netacad.com/courses/programming/pcap-programming-essentials-python

4. Attempt for this certification
https://freecodecamp.org/learn/scientific-computing-with-python/

5. Take it to next level

❯ Data Scrapping, NumPy, Pandas
https://scaler.com/topics/course/python-for-data-science/

❯ Data Analysis
https://openclassrooms.com/courses/2304731-learn-python-basics-for-data-analysis

❯ Data Visualization
https://kaggle.com/learn/data-visualization

❯ Django
https://openclassrooms.com/courses/6967196-create-a-web-application-with-django

❯ Machine Learning
https://developers.google.com/machine-learning/crash-course

❯ Deep Learning (TensorFlow)
https://kaggle.com/learn/intro-to-deep-learning

https://t.iss.one/CodeProgrammer

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🐕 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

https://t.iss.one/DataScienceT

🔥 Master Data Science for free

📂 Computer Science 101
https://online.stanford.edu/courses/soe-ycscs101-computer-science-101

📂 Machine Learning Specialization
https://coursera.org/specializations/machine-learning-introduction

📂 Artificial Intelligence for Robotics
https://udacity.com/course/artificial-intelligence-for-robotics--cs373

📂 Designing Your Career
https://online.stanford.edu/courses/tds-y0003-designing-your-career

📂 Stanford: Game Theory
https://online.stanford.edu/courses/soe-ycs0002-game-theory

📂 Machine Learning with Python
https://www.freecodecamp.org/learn/machine-learning-with-python/

📂 Probability and Statistics: To P or Not To P? (Coursera)
https://www.coursera.org/learn/probability-statistics

📂 Numpy complete free course
https://www.youtube.com/playlist?list=PLysMDSbb9Hcz3Gdi9oV-btohZ9zhths-r

📂Advanced Machine Learning
https://www.kaggle.com/learn/intro-to-machine-learning

📂 Stat 110: Harvard University (YouTube)
https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo&index=1

📂 The Open Source Data Science Masters
https://github.com/datasciencemasters/go

📂 Google - artificial intelligence for everyone
https://edx.org/learn/artificial-intelligence/google-google-ai-for-anyone

📂Microsoft - AI for Beginners
https://microsoft.github.io/AI-For-Beginners

📂 IBM - AI for Everyone: Master the Basics
https://edx.org/learn/artificial-intelligence/ibm-ai-for-everyone-master-the-basics

📂 Harvard - Introduction to Artificial Intelligence with Python
https://cs50.harvard.edu/ai/2023

📂 Introduction to Generative AI
https://cloudskillsboost.google/journeys/118

📂 Deep Learning - Finetuning Large Language Models
https://deeplearning.ai/short-courses/finetuning-large-language-models/

📂Microsoft - AI Basics in Azure
https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/

https://t.iss.one/CodeProgrammer

📂Linux Foundation
https://edx.org/learn/computer-programming/the-linux-foundation-data-and-ai-fundamentals

📂 12 Linux courses:
https://t.iss.one/linuxkalii/538

📂 Alison - 13 free AI courses
https://alison.com/tag/artificial-intelligence

📂 Artificial Intelligence Projects:
https://mygreatlearning.com/academy/learn-for-free/courses/artificial-intelligence-projects

📂 Introduction to Internet of Things:
https://online.stanford.edu/courses/xee100-introduction-internet-things

📂 Graph Search, Shortest Paths, and Data Structures
https://coursera.org/learn/algorithms-graphs-data-structures

📂 Python:
https://cs50.harvard.edu/python/2022/

📂 Machine Learning:
https://developers.google.com/machine-learning/crash-course

📂 Deep Learning
https://introtodeeplearning.com

📂 Data Analysis
https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis

📂 Linear algebra:
https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra

📂 Algebra basics
https://www.khanacademy.org/math/algebra-basics

📂 Excel and PowerBI
https://learn.microsoft.com/training/paths/modern-analytics/

📂 Data visualization:
https://pll.harvard.edu/course/data-science-visualization

📂 PowerBI
https://learn.microsoft.com/users/collinschedler-0717/collections/m14nt4rdwnwp04

📂 Tableau:
https://tableau.com/learn/training

📂 Statistics:
https://cognitiveclass.ai/courses/statistics-101

📂 SQL:
https://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://t.iss.one/CodeProgrammer

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imbalanced-DL: Deep Imbalanced Learning in Python

🖥 Github: https://github.com/ntucllab/imbalanced-dl

📕 Paper: https://arxiv.org/pdf/2308.15457v1.pdf

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

https://t.iss.one/DataScienceT

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

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

✅ https://t.iss.one/DataScienceM

🖥 Free Courses and Guides That Will Teach You How to Master AI:

📂 Elements of AI
https://elementsofai.com

📂 Learn Prompting
https://learnprompting.org

📂 Machine Learning
https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning

📂 AI for everyone
https://coursera.org/learn/ai-for-everyone

📂 500+ AI Chatbot Prompt Templates
https://theveller.gumroad.com/l/ChatGPTPromptTemplates-byTheVeller

📂 Prompt Engineering
https://youtu.be/_ZvnD73m40o

📂 ChatGPT Prompt Engineering for Developers
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers

📂 Google — AI for Anyone
https://edx.org/learn/artificial-intelligence/google-google-ai-for-anyone

📂 Microsoft — AI For Beginners
https://microsoft.github.io/AI-For-Beginners

📂 IBM — AI for Everyone: Master the Basics
https://edx.org/learn/artificial-intelligence/ibm-ai-for-everyone-master-the-basics

📂 Google — Introduction to Generative AI
https://cloudskillsboost.google/journeys/118

📂 DeepLearning — Finetuning LLMs
https://deeplearning.ai/short-courses/finetuning-large-language-models

https://t.iss.one/CodeProgrammer

Please more 100 👍 with our posts

Here's a list of 50+ Python libraries for data science👇

1. NumPy - "Handles arrays and math operations efficiently."
2. pandas - "Data manipulation made easy with data frames."
3. Matplotlib - "Plots and charts for data visualization."
4. Seaborn - "Creates attractive statistical plots."
5. SciPy - "Scientific and technical computing toolkit."
6. scikit-learn - "Machine learning at your fingertips."
7. TensorFlow - "For deep learning and neural networks."
8. Keras - "High-level deep learning API."
9. PyTorch - "Deep learning framework for researchers."
10. Statsmodels - "Statistical models and tests."
11. NLTK - "Natural language processing toolkit."
12. Gensim - "Topic modeling and document similarity."
13. XGBoost - "Gradient boosting for better predictions."
14. LightGBM - "Efficient gradient boosting framework."
15. CatBoost - "Optimized gradient boosting for categories."
16. NetworkX - "Build and analyze networks and graphs."
17. Beautiful Soup - "HTML and XML parsing made simple."
18. Requests - "Effortless HTTP requests."
19. SQLAlchemy - "Relational database interactions."
20. Pandas Profiling - "Generate data reports quickly."
21. Featuretools - "Automated feature engineering."
22. H2O - "Open-source machine learning platform."
23. Yellowbrick - "Visualize machine learning results."
24. Plotly - "Interactive and shareable plots."
25. Dash - "Build web apps for data visualization."
26. Flask - "Lightweight web app framework."
27. Streamlit - "Create apps with minimal code."
28. Bokeh - "Interactive web-based visualization."
29. GeoPandas - "Geospatial data analysis made easy."
30. Altair - "Declarative statistical visualization."
31. Prophet - "Time series forecasting with ease."
32. Feature-engine - "Feature engineering for ML."
33. Dask - "Parallel computing for big data."
34. Vaex - "Efficient dataframes for big data."
35. Optuna - "Automated hyperparameter tuning."
36. imbalanced-learn - "Handling imbalanced datasets."
37. Eli5 - "Interpret machine learning models."
38. SHAP - "Explainability for ML models."
39. scikit-image - "Image processing in Python."
40. TextBlob - "Text processing and sentiment analysis."
41. Polars - "Fast DataFrame library."
42. Cufflinks - "Combines Plotly with pandas."
43. TA-Lib - "Technical analysis for financial data."
44. OpenCV - "Computer vision and image processing."
45. Pymc3 - "Probabilistic programming for Bayesian analysis."
46. Scrapy - "Web scraping toolkit."
47. PySpark - "Apache Spark for big data processing."
48. PyArrow - "Columnar data format for analytics."
49. OptimalFlow - "AutoML for data scientists."
50. Pycaret - "Automated machine learning toolkit."

These libraries cover a wide range of data science tasks, from data manipulation and visualisation to machine learning and deep learning, making them essential tools for any data scientist or Python programmer.

https://t.iss.one/DataScienceT

ResFields: Residual Neural Fields for Spatiotemporal Signals

🖥 Github: https://github.com/markomih/ResFields

📕 Paper: https://arxiv.org/pdf/2309.03160.pdf

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

https://t.iss.one/DataScienceT