๐โณThese 6 steps make every future post on LLMs instantly clear and meaningful.
Learn exactly where Web Scraping, Tokenization, RLHF, Transformer Architectures, ONNX Optimization, Causal Language Modeling, Gradient Clipping, Adaptive Learning, Supervised Fine-Tuning, RLAIF, TensorRT Inference, and more fit into the LLM pipeline.
๏น๏น๏น๏น๏น๏น๏น๏น๏น
ใ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐๐ ๐: ๐ง๐ต๐ฒ ๐ฒ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฆ๐๐ฒ๐ฝ๐
โธ 1๏ธโฃ Data Collection (Web Scraping & Curation)
โ Web Scraping: Gather data from books, research papers, Wikipedia, GitHub, Reddit, and more using Scrapy, BeautifulSoup, Selenium, and APIs.
โ Filtering & Cleaning: Remove duplicates, spam, broken HTML, and filter biased, copyrighted, or inappropriate content.
โ Dataset Structuring: Tokenize text using BPE, SentencePiece, or Unigram; add metadata like source, timestamp, and quality rating.
โธ 2๏ธโฃ Preprocessing & Tokenization
โ Tokenization: Convert text into numerical tokens using SentencePiece or GPTโs BPE tokenizer.
โ Data Formatting: Structure datasets into JSON, TFRecord, or Hugging Face formats; use Sharding for parallel processing.
โธ 3๏ธโฃ Model Architecture & Pretraining
โ Architecture Selection: Choose a Transformer-based model (GPT, T5, LLaMA, Falcon) and define parameter size (7Bโ175B).
โ Compute & Infrastructure: Train on GPUs/TPUs (A100, H100, TPU v4/v5) with PyTorch, JAX, DeepSpeed, and Megatron-LM.
โ Pretraining: Use Causal Language Modeling (CLM) with Cross-Entropy Loss, Gradient Checkpointing, and Parallelization (FSDP, ZeRO).
โ Optimizations: Apply Mixed Precision (FP16/BF16), Gradient Clipping, and Adaptive Learning Rate Schedulers for efficiency.
โธ 4๏ธโฃ Model Alignment (Fine-Tuning & RLHF)
โ Supervised Fine-Tuning (SFT): Train on high-quality human-annotated datasets (InstructGPT, Alpaca, Dolly).
โ Reinforcement Learning from Human Feedback (RLHF): Generate responses, rank outputs, train a Reward Model (PPO), and refine using Proximal Policy Optimization (PPO).
โ Safety & Constitutional AI: Apply RLAIF, adversarial training, and bias filtering.
โธ 5๏ธโฃ Deployment & Optimization
โ Compression & Quantization: Reduce model size with GPTQ, AWQ, LLM.int8(), and Knowledge Distillation.
โ API Serving & Scaling: Deploy with vLLM, Triton Inference Server, TensorRT, ONNX, and Ray Serve for efficient inference.
โ Monitoring & Continuous Learning: Track performance, latency, and hallucinations;
โธ 6๏ธโฃEvaluation & Benchmarking
โ Performance Testing: Validate using HumanEval, HELM, OpenAI Eval, MMLU, ARC, and MT-Bench.
โฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃ
https://t.iss.one/DataScienceMโญ๏ธ
Learn exactly where Web Scraping, Tokenization, RLHF, Transformer Architectures, ONNX Optimization, Causal Language Modeling, Gradient Clipping, Adaptive Learning, Supervised Fine-Tuning, RLAIF, TensorRT Inference, and more fit into the LLM pipeline.
๏น๏น๏น๏น๏น๏น๏น๏น๏น
ใ ๐๐๐ถ๐น๐ฑ๐ถ๐ป๐ด ๐๐๐ ๐: ๐ง๐ต๐ฒ ๐ฒ ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น ๐ฆ๐๐ฒ๐ฝ๐
โธ 1๏ธโฃ Data Collection (Web Scraping & Curation)
โ Web Scraping: Gather data from books, research papers, Wikipedia, GitHub, Reddit, and more using Scrapy, BeautifulSoup, Selenium, and APIs.
โ Filtering & Cleaning: Remove duplicates, spam, broken HTML, and filter biased, copyrighted, or inappropriate content.
โ Dataset Structuring: Tokenize text using BPE, SentencePiece, or Unigram; add metadata like source, timestamp, and quality rating.
โธ 2๏ธโฃ Preprocessing & Tokenization
โ Tokenization: Convert text into numerical tokens using SentencePiece or GPTโs BPE tokenizer.
โ Data Formatting: Structure datasets into JSON, TFRecord, or Hugging Face formats; use Sharding for parallel processing.
โธ 3๏ธโฃ Model Architecture & Pretraining
โ Architecture Selection: Choose a Transformer-based model (GPT, T5, LLaMA, Falcon) and define parameter size (7Bโ175B).
โ Compute & Infrastructure: Train on GPUs/TPUs (A100, H100, TPU v4/v5) with PyTorch, JAX, DeepSpeed, and Megatron-LM.
โ Pretraining: Use Causal Language Modeling (CLM) with Cross-Entropy Loss, Gradient Checkpointing, and Parallelization (FSDP, ZeRO).
โ Optimizations: Apply Mixed Precision (FP16/BF16), Gradient Clipping, and Adaptive Learning Rate Schedulers for efficiency.
โธ 4๏ธโฃ Model Alignment (Fine-Tuning & RLHF)
โ Supervised Fine-Tuning (SFT): Train on high-quality human-annotated datasets (InstructGPT, Alpaca, Dolly).
โ Reinforcement Learning from Human Feedback (RLHF): Generate responses, rank outputs, train a Reward Model (PPO), and refine using Proximal Policy Optimization (PPO).
โ Safety & Constitutional AI: Apply RLAIF, adversarial training, and bias filtering.
โธ 5๏ธโฃ Deployment & Optimization
โ Compression & Quantization: Reduce model size with GPTQ, AWQ, LLM.int8(), and Knowledge Distillation.
โ API Serving & Scaling: Deploy with vLLM, Triton Inference Server, TensorRT, ONNX, and Ray Serve for efficient inference.
โ Monitoring & Continuous Learning: Track performance, latency, and hallucinations;
โธ 6๏ธโฃEvaluation & Benchmarking
โ Performance Testing: Validate using HumanEval, HELM, OpenAI Eval, MMLU, ARC, and MT-Bench.
โฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃโฃ
https://t.iss.one/DataScienceM
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html-to-markdown
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
โญ๏ธ Full HTML5 Support: Comprehensive support for all modern HTML5 elements including semantic, form, table, ruby, interactive, structural, SVG, and math elements
โญ๏ธ Enhanced Table Support: Advanced handling of merged cells with rowspan/colspan support for better table representation
โญ๏ธ Type Safety: Strict MyPy adherence with comprehensive type hints
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
โญ๏ธ Streaming Support: Memory-efficient processing for large documents with progress callbacks
โญ๏ธ Highlight Support: Multiple styles for highlighted text (<mark> elements)
โญ๏ธ Task List Support: Converts HTML checkboxes to GitHub-compatible task list syntax
nstallation
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
The lxml parser offers:
๐ ~30% faster HTML parsing compared to the default html.parser
๐ Better handling of malformed HTML
๐ More robust parsing for complex documents
Quick Start
Convert HTML to Markdown with a single function call:
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
Github: https://github.com/Goldziher/html-to-markdown
https://t.iss.one/DataScienceNโญ๏ธ
A modern, fully typed Python library for converting HTML to Markdown. This library is a completely rewritten fork of markdownify with a modernized codebase, strict type safety and support for Python 3.9+.
Features:
Metadata Extraction: Automatic extraction of document metadata (title, meta tags) as comment headers
nstallation
pip install html-to-markdown
Optional lxml Parser
For improved performance, you can install with the optional lxml parser:
pip install html-to-markdown[lxml]
The lxml parser offers:
Quick Start
Convert HTML to Markdown with a single function call:
from html_to_markdown import convert_to_markdown
html = """
<!DOCTYPE html>
<html>
<head>
<title>Sample Document</title>
<meta name="description" content="A sample HTML document">
</head>
<body>
<article>
<h1>Welcome</h1>
<p>This is a <strong>sample</strong> with a <a href="https://example.com">link</a>.</p>
<p>Here's some <mark>highlighted text</mark> and a task list:</p>
<ul>
<li><input type="checkbox" checked> Completed task</li>
<li><input type="checkbox"> Pending task</li>
</ul>
</article>
</body>
</html>
"""
markdown = convert_to_markdown(html)
print(markdown)
Working with BeautifulSoup:
If you need more control over HTML parsing, you can pass a pre-configured BeautifulSoup instance:
from bs4 import BeautifulSoup
from html_to_markdown import convert_to_markdown
# Configure BeautifulSoup with your preferred parser
soup = BeautifulSoup(html, "lxml") # Note: lxml requires additional installation
markdown = convert_to_markdown(soup)
Github: https://github.com/Goldziher/html-to-markdown
https://t.iss.one/DataScienceN
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LangExtract
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.iss.one/DataScience4๐
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
GitHub: https://github.com/google/langextract
https://t.iss.one/DataScience4
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0๏ธโฃ Python
1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
3๏ธโฃ Data Visualization
4๏ธโฃ Artificial Intelligence
5๏ธโฃ Data Analysis
6๏ธโฃ Statistics
7๏ธโฃ Deep Learning
8๏ธโฃ programming Languages
โ
https://t.iss.one/addlist/8_rRW2scgfRhOTc0
โ
https://t.iss.one/Codeprogrammer
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โ
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Researchers trained the model on 70 hours of Minecraft gameplay and achieved impressive results:
GameFactory can create procedural game worlds โ from volcanoes to cherry blossom forests, just like in the iconic simulator.
https://t.iss.one/DataScienceN
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python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://t.iss.one/DataScienceN๐
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
https://t.iss.one/DataScienceN
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Data scientists, this is for you โ I dug up LeetCode for DS
DataLemur โ a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here๐ฏ
๐ https://t.iss.one/DataScienceN ๐
DataLemur โ a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here
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๐ฅ Trending Repository: Deep-Learning-Roadmap
๐ Description: :satellite: Organized Resources for Deep Learning Researchers and Developers
๐ Repository URL: https://github.com/astorfi/Deep-Learning-Roadmap
๐ Website: https://machinelearningmindset.com/deep-learning-resources/
๐ Readme: https://github.com/astorfi/Deep-Learning-Roadmap#readme
๐ Statistics:
๐ Stars: 3.2K stars
๐ Watchers: 144
๐ด Forks: 314 forks
๐ป Programming Languages: Python
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: :satellite: Organized Resources for Deep Learning Researchers and Developers
๐ Repository URL: https://github.com/astorfi/Deep-Learning-Roadmap
๐ Website: https://machinelearningmindset.com/deep-learning-resources/
๐ Readme: https://github.com/astorfi/Deep-Learning-Roadmap#readme
๐ Statistics:
๐ Stars: 3.2K stars
๐ Watchers: 144
๐ด Forks: 314 forks
๐ป Programming Languages: Python
๐ท๏ธ Related Topics:
#reinforcement_learning #deep_learning
==================================
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๐ฅ Trending Repository: awesome-transformer-nlp
๐ Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
๐ Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
๐ Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
๐ Statistics:
๐ Stars: 1.1K stars
๐ Watchers: 41
๐ด Forks: 131 forks
๐ป Programming Languages: Not available
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==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
๐ Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
๐ Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
๐ Statistics:
๐ Stars: 1.1K stars
๐ Watchers: 41
๐ด Forks: 131 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: SemanticSegmentation_DL
๐ Description: Resources of semantic segmantation based on Deep Learning model
๐ Repository URL: https://github.com/tangzhenyu/SemanticSegmentation_DL
๐ Readme: https://github.com/tangzhenyu/SemanticSegmentation_DL#readme
๐ Statistics:
๐ Stars: 1.1K stars
๐ Watchers: 77
๐ด Forks: 315 forks
๐ป Programming Languages: Jupyter Notebook - Python - Shell - sed
๐ท๏ธ Related Topics: Not available
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: Resources of semantic segmantation based on Deep Learning model
๐ Repository URL: https://github.com/tangzhenyu/SemanticSegmentation_DL
๐ Readme: https://github.com/tangzhenyu/SemanticSegmentation_DL#readme
๐ Statistics:
๐ Stars: 1.1K stars
๐ Watchers: 77
๐ด Forks: 315 forks
๐ป Programming Languages: Jupyter Notebook - Python - Shell - sed
๐ท๏ธ Related Topics: Not available
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ฅ Trending Repository: awesome-jetpack-compose-learning-resources
๐ Description: ๐ A continuously updated list of learning Jetpack Compose for Android apps.
๐ Repository URL: https://github.com/androiddevnotes/awesome-jetpack-compose-learning-resources
๐ Readme: https://github.com/androiddevnotes/awesome-jetpack-compose-learning-resources#readme
๐ Statistics:
๐ Stars: 1.4K stars
๐ Watchers: 41
๐ด Forks: 140 forks
๐ป Programming Languages: Kotlin
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceM
๐ Description: ๐ A continuously updated list of learning Jetpack Compose for Android apps.
๐ Repository URL: https://github.com/androiddevnotes/awesome-jetpack-compose-learning-resources
๐ Readme: https://github.com/androiddevnotes/awesome-jetpack-compose-learning-resources#readme
๐ Statistics:
๐ Stars: 1.4K stars
๐ Watchers: 41
๐ด Forks: 140 forks
๐ป Programming Languages: Kotlin
๐ท๏ธ Related Topics:
#android #kotlin #awesome #mvvm #android_architecture #compose #beginner_friendly #android_apps #hacktoberfest #coroutines_android #mvvm_android #android_jetpack #first_issue #jetpack_android #learn_android #jetpack_compose #hacktoberfest2020 #android_compose #awesome_android
==================================
๐ง By: https://t.iss.one/DataScienceM
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๐ฅ Trending Repository: awesome-learning
๐ Description: A curated list for DevOps learning resources. Join the slack channel to discuss more.
๐ Repository URL: https://github.com/Lets-DevOps/awesome-learning
๐ Readme: https://github.com/Lets-DevOps/awesome-learning#readme
๐ Statistics:
๐ Stars: 920 stars
๐ Watchers: 43
๐ด Forks: 310 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: A curated list for DevOps learning resources. Join the slack channel to discuss more.
๐ Repository URL: https://github.com/Lets-DevOps/awesome-learning
๐ Readme: https://github.com/Lets-DevOps/awesome-learning#readme
๐ Statistics:
๐ Stars: 920 stars
๐ Watchers: 43
๐ด Forks: 310 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#infrastructure #learning #devops
==================================
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๐ฅ Trending Repository: Machine-Learning-Tutorials
๐ Description: machine learning and deep learning tutorials, articles and other resources
๐ Repository URL: https://github.com/ujjwalkarn/Machine-Learning-Tutorials
๐ Website: https://ujjwalkarn.github.io/Machine-Learning-Tutorials
๐ Readme: https://github.com/ujjwalkarn/Machine-Learning-Tutorials#readme
๐ Statistics:
๐ Stars: 16.6K stars
๐ Watchers: 797
๐ด Forks: 3.9K forks
๐ป Programming Languages: Not available
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==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: machine learning and deep learning tutorials, articles and other resources
๐ Repository URL: https://github.com/ujjwalkarn/Machine-Learning-Tutorials
๐ Website: https://ujjwalkarn.github.io/Machine-Learning-Tutorials
๐ Readme: https://github.com/ujjwalkarn/Machine-Learning-Tutorials#readme
๐ Statistics:
๐ Stars: 16.6K stars
๐ Watchers: 797
๐ด Forks: 3.9K forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#list #machine_learning #awesome #deep_neural_networks #deep_learning #neural_network #neural_networks #awesome_list #machinelearning #deeplearning #deep_learning_tutorial
==================================
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๐ฅ Trending Repository: awesome-recursion-schemes
๐ Description: Resources for learning and using recursion schemes.
๐ Repository URL: https://github.com/passy/awesome-recursion-schemes
๐ Readme: https://github.com/passy/awesome-recursion-schemes#readme
๐ Statistics:
๐ Stars: 1.3K stars
๐ Watchers: 44
๐ด Forks: 56 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: Resources for learning and using recursion schemes.
๐ Repository URL: https://github.com/passy/awesome-recursion-schemes
๐ Readme: https://github.com/passy/awesome-recursion-schemes#readme
๐ Statistics:
๐ Stars: 1.3K stars
๐ Watchers: 44
๐ด Forks: 56 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#awesome #recursion_schemes #catamorphisms
==================================
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๐ฅ Trending Repository: awesome-deeplearning-resources
๐ Description: Deep Learning and deep reinforcement learning research papers and some codes
๐ Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
๐ Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
๐ Statistics:
๐ Stars: 2.9K stars
๐ Watchers: 221
๐ด Forks: 666 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: Deep Learning and deep reinforcement learning research papers and some codes
๐ Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
๐ Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
๐ Statistics:
๐ Stars: 2.9K stars
๐ Watchers: 221
๐ด Forks: 666 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ฅ Trending Repository: Machine_Learning_Resources
๐ Description: :fish::fish::fish: ๆบๅจๅญฆไน ้ข่ฏๅคไน ่ตๆบ
๐ Repository URL: https://github.com/wangyuGithub01/Machine_Learning_Resources
๐ Readme: https://github.com/wangyuGithub01/Machine_Learning_Resources#readme
๐ Statistics:
๐ Stars: 1.2K stars
๐ Watchers: 10
๐ด Forks: 179 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics: Not available
==================================
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๐ Description: :fish::fish::fish: ๆบๅจๅญฆไน ้ข่ฏๅคไน ่ตๆบ
๐ Repository URL: https://github.com/wangyuGithub01/Machine_Learning_Resources
๐ Readme: https://github.com/wangyuGithub01/Machine_Learning_Resources#readme
๐ Statistics:
๐ Stars: 1.2K stars
๐ Watchers: 10
๐ด Forks: 179 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics: Not available
==================================
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๐ฅ Trending Repository: Awesome-Meta-Learning
๐ Description: A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
๐ Repository URL: https://github.com/sudharsan13296/Awesome-Meta-Learning
๐ Readme: https://github.com/sudharsan13296/Awesome-Meta-Learning#readme
๐ Statistics:
๐ Stars: 1.5K stars
๐ Watchers: 68
๐ด Forks: 298 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: A curated list of Meta Learning papers, code, books, blogs, videos, datasets and other resources.
๐ Repository URL: https://github.com/sudharsan13296/Awesome-Meta-Learning
๐ Readme: https://github.com/sudharsan13296/Awesome-Meta-Learning#readme
๐ Statistics:
๐ Stars: 1.5K stars
๐ Watchers: 68
๐ด Forks: 298 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#one_shot_learning #zero_shot_learning #metalearning #few_shot_learning #deep_meta_learning #meta_reinforcement
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ฅ Trending Repository: programming-math-science
๐ Description: This is a list of links to different freely available learning resources about computer programming, math, and science.
๐ Repository URL: https://github.com/bobeff/programming-math-science
๐ Readme: https://github.com/bobeff/programming-math-science#readme
๐ Statistics:
๐ Stars: 1.8K stars
๐ Watchers: 26
๐ด Forks: 129 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
==================================
๐ง By: https://t.iss.one/DataScienceN
๐ Description: This is a list of links to different freely available learning resources about computer programming, math, and science.
๐ Repository URL: https://github.com/bobeff/programming-math-science
๐ Readme: https://github.com/bobeff/programming-math-science#readme
๐ Statistics:
๐ Stars: 1.8K stars
๐ Watchers: 26
๐ด Forks: 129 forks
๐ป Programming Languages: Not available
๐ท๏ธ Related Topics:
#science #programming #math #awesome_list
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๐ง By: https://t.iss.one/DataScienceN