💻PyGraft: Configurable Generation of Schemas and Knowledge Graphs at Your Fingertips
🖥 Github: https://github.com/nicolas-hbt/pygraft
📕 Paper: https://arxiv.org/abs/2309.03685
⭐️ Docs: https://pygraft.readthedocs.io/en/latest/
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/nicolas-hbt/pygraft
📕 Paper: https://arxiv.org/abs/2309.03685
⭐️ Docs: https://pygraft.readthedocs.io/en/latest/
https://t.iss.one/DataScienceT
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Forwarded from Eng. Hussein Sheikho 👨💻
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
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
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Forwarded from Python | Machine Learning | Coding | R
🖥 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
📂 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
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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
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
❤22👍13🏆5
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
🖥 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
❤16👍8
🚩 Towards the TopMost: A Topic Modeling System Toolkit
The highly cohesive and decoupled modular design of TopMost enables quick utilization, fair comparisons, and flexible extensions of different topic models.
🖥 Github: https://github.com/bobxwu/topmost
📕 Paper: https://arxiv.org/abs/2309.06908v1
⏩ Docs: https://topmost.readthedocs.io/
⭐️ Dataset: https://paperswithcode.com/dataset/imdb-movie-reviews
https://t.iss.one/DataScienceT
The highly cohesive and decoupled modular design of TopMost enables quick utilization, fair comparisons, and flexible extensions of different topic models.
$ pip install topmost🖥 Github: https://github.com/bobxwu/topmost
📕 Paper: https://arxiv.org/abs/2309.06908v1
⏩ Docs: https://topmost.readthedocs.io/
⭐️ Dataset: https://paperswithcode.com/dataset/imdb-movie-reviews
https://t.iss.one/DataScienceT
❤9👍5
📂 An Open-source Framework for Autonomous Language Agents
Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control.
🖥 Github: https://github.com/aiwaves-cn/agents
📕 Paper: https://arxiv.org/pdf/2309.07870.pdf
⏩ Demo: https://github.com/aiwaves-cn/agents#web-demos
⭐️ Project: https://www.aiwaves-agents.com/
https://t.iss.one/DataScienceT
Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control.
pip install ai-agents🖥 Github: https://github.com/aiwaves-cn/agents
📕 Paper: https://arxiv.org/pdf/2309.07870.pdf
⏩ Demo: https://github.com/aiwaves-cn/agents#web-demos
⭐️ Project: https://www.aiwaves-agents.com/
https://t.iss.one/DataScienceT
❤7👍6
LightTBNet
🖥 Github: https://github.com/dani-capellan/LightTBNet
📕 Paper: https://arxiv.org/pdf/2309.02140v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/montgomery-county-x-ray-set
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/dani-capellan/LightTBNet
📕 Paper: https://arxiv.org/pdf/2309.02140v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/montgomery-county-x-ray-set
https://t.iss.one/DataScienceT
👍10❤4
Forwarded from Data Science Books
We have just launched a fundraising campaign for the channel to ensure continued quality service
We upload the book via Internet data, and this is expensive for us
Participate and contribute to the donation campaign until the target amount is reached
Members who will contribute to the donation campaign will receive a free subscription to the paid channel and a LinkedIn grant
Donate link:
https://boosty.to/datascienceteam/donate
We upload the book via Internet data, and this is expensive for us
Participate and contribute to the donation campaign until the target amount is reached
Members who will contribute to the donation campaign will receive a free subscription to the paid channel and a LinkedIn grant
Donate link:
https://boosty.to/datascienceteam/donate
👍3
💥 MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
MMICL is a multimodal vision-language model with the ability to analyze and understand multiple images, as well as follow instructions.
🖥 Github: https://github.com/haozhezhao/mic
📕 Paper: https://arxiv.org/abs/2309.07915v1
⭐️ Datasets: https://paperswithcode.com/dataset/mmbench
https://t.iss.one/DataScienceT
MMICL is a multimodal vision-language model with the ability to analyze and understand multiple images, as well as follow instructions.
🖥 Github: https://github.com/haozhezhao/mic
📕 Paper: https://arxiv.org/abs/2309.07915v1
⭐️ Datasets: https://paperswithcode.com/dataset/mmbench
https://t.iss.one/DataScienceT
👍4
LLM_Fine_Tuning_Molecular_Properties
🖥 Github: https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties
📕 Paper: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65030b55b338ec988a780108/original/chem-ber-ta-2-fine-tuning-for-molecule-s-hiv-replication-inhibition-prediction.pdf
🔥 Dataset: https://paperswithcode.com/dataset/moleculenet
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/SylwiaNowakowska/LLM_Fine_Tuning_Molecular_Properties
📕 Paper: https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/65030b55b338ec988a780108/original/chem-ber-ta-2-fine-tuning-for-molecule-s-hiv-replication-inhibition-prediction.pdf
🔥 Dataset: https://paperswithcode.com/dataset/moleculenet
https://t.iss.one/DataScienceT
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🦙 LLaVA: Large Language and Vision Assistant
🖥 Github: https://github.com/haotian-liu/LLaVA
📕 Paper: https://arxiv.org/pdf/2309.09958v1.pdf
⭐️ Datasets: https://paperswithcode.com/dataset/mmlu
https://t.iss.one/DataScienceT
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA🖥 Github: https://github.com/haotian-liu/LLaVA
📕 Paper: https://arxiv.org/pdf/2309.09958v1.pdf
⭐️ Datasets: https://paperswithcode.com/dataset/mmlu
https://t.iss.one/DataScienceT
👍7❤2
LLMKE
🖥 Github: https://github.com/bohuizhang/llmke
📕 Paper: https://arxiv.org/pdf/2309.08491v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/lama
https://t.iss.one/DataScienceT
🖥 Github: https://github.com/bohuizhang/llmke
📕 Paper: https://arxiv.org/pdf/2309.08491v1.pdf
🔥 Dataset: https://paperswithcode.com/dataset/lama
https://t.iss.one/DataScienceT
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