Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".
https://www.k-a.in/pyt-transformer.html
This guide offers:
By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.
#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks
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Four best-advanced university courses on NLP & LLM to advance your skills:
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
#LLM #python #AI #Agents #RAG #NLP
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
1. Advanced NLP -- Carnegie Mellon University
Link: https://lnkd.in/ddEtMghr
2. Recent Advances on Foundation Models -- University of Waterloo
Link: https://lnkd.in/dbdpUV9v
3. Large Language Model Agents -- University of California, Berkeley
Link: https://lnkd.in/d-MdSM8Y
4. Advanced LLM Agent -- University Berkeley
Link: https://lnkd.in/dvCD4HR4
#LLM #python #AI #Agents #RAG #NLP
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Full PyTorch Implementation of Transformer-XL
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://yangx.top/CodeProgrammer
If you're looking to understand and experiment with Transformer-XL using PyTorch, this resource provides a clean and complete implementation. Transformer-XL is a powerful model that extends the Transformer architecture with recurrence, enabling learning dependencies beyond fixed-length segments.
The implementation is ideal for researchers, students, and developers aiming to dive deeper into advanced language modeling techniques.
Explore the code and start building:
https://www.k-a.in/pyt-transformerXL.html
#TransformerXL #PyTorch #DeepLearning #NLP #LanguageModeling #AI #MachineLearning #OpenSource #ResearchTools
https://yangx.top/CodeProgrammer
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Introduction to Machine Learning” by Alex Smola and S.V.N.
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning #MathForML #MLTheory #MLResearch #AlexSmola #SVNVishwanathan
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Machine Learning Notes 📝 (1).pdf
4.9 MB
Machine Learning Notes with Real Project and Amazing discussion
https://yangx.top/CodeProgrammer🌟
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning
https://yangx.top/CodeProgrammer
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These 9 courses covers LLMs, Agents, Deep RL, Audio and more
https://huggingface.co/learn/llm-course/chapter1/1
https://huggingface.co/learn/agents-course/unit0/introduction
https://huggingface.co/learn/deep-rl-course/unit0/introduction
https://huggingface.co/learn/cookbook/index
https://huggingface.co/learn/ml-games-course/unit0/introduction
https://huggingface.co/learn/audio-course/chapter0/introduction
https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
https://huggingface.co/learn/ml-for-3d-course/unit0/introduction
https://huggingface.co/learn/diffusion-course/unit0/1
#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAI
Join to our WhatsApp
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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@codeprogrammer machine learning notes.pdf
21 MB
Best Machine Learning Notes
Join to our WhatsApp📱 channel:
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
#HuggingFace #FreeCourses #AI #MachineLearning #DeepLearning #LLM #Agents #python #PythonProgramming #ReinforcementLearning #AudioAI #ComputerVision #3DAI #DiffusionModels #OpenSourceAI
Join to our WhatsApp
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1️⃣ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2️⃣ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3️⃣ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4️⃣ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6️⃣ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7️⃣ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8️⃣ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9️⃣ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
🔗 Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization
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A new interactive sentiment visualization project has been developed, featuring a dynamic smiley face that reflects sentiment analysis results in real time. Using a natural language processing model, the system evaluates input text and adjusts the smiley face expression accordingly:
🙂 Positive sentiment
☹️ Negative sentiment
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
🔗 GitHub: https://lnkd.in/e_gk3hfe
📰 Article: https://lnkd.in/e_baNJd2
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
🔗 Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
The visualization offers an intuitive and engaging way to observe sentiment dynamics as they happen.
#AI #SentimentAnalysis #DataVisualization #InteractiveDesign #NLP #MachineLearning #Python #GitHubProjects #TowardsDataScience
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