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โ๏ธ Pyneng โ a large base for Python and network automation!
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
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Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
๐ I'll leave a link: https://pyneng.readthedocs.io/en/latest/
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
โ 13 courses live + 40+ coming soon
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๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
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The ultimate guide to fine tuning.pdf
15.2 MB
๐ The Big Book on Fine-Tuning LLMs
A free 115-page book dedicated to the retraining of large language models. ๐
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. ๐
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โ 13 courses live + 40+ coming soon
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๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
A free 115-page book dedicated to the retraining of large language models. ๐
It's suitable for those who want to understand how to prepare datasets, configure training, and improve the quality of LLMs for their tasks. ๐
#LLM #FineTuning #AI #MachineLearning #DataScience #Tech
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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Data Science Interview Questions.pdf
1.4 MB
Data Science Interview Questions
๐ก Here is your curated list for Data Science interviews!
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๐ Lifetime access to HelloEncyclo โ every AI, ML & Data Science course ever built โ for ~$41. Once. Forever.
This isn't a drill. This isn't a rerun.
This is the founding-member price โ and it disappears the moment the first 250 seats globally are gone.
โ 13 courses live right now
โ 40+ more in 2โ3 weeks
โ Every future course included automatically
โ 15-day money-back โ full refund, no questions
Code: PRESALE-BOOK-WAVE-2GFG
(Log in with Gmail ยท valid once ยท applies at checkout)
๐ First 5. That's it.
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A new collection of free courses has been added:
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
๐ https://github.com/dair-ai/ML-Course-Notes
Those studying ML through dozens of random tabs and unclosed playlists may find this repository useful for organizing their learning. ๐
Machine Learning Course Notes is an open collection of notes on machine learning, NLP, and AI, compiled around full-fledged courses, not just individual videos. ๐ง
What's inside:
โข Courses from the Machine Learning Specialization, MIT 6.S191, CMU Neural Nets for NLP, CS224N, CS25, and others
โข A table with lectures, descriptions, videos, notes, and authors
โข Links to the original lectures and accompanying notes
โข WIP markers for incomplete materials
โข Instructions for contributors on adding and improving notes
The idea was appreciated. ๐
Instead of another collection of hundreds of links, a course map has been created where one can systematically go through the material without getting lost after a week of studying. ๐บ๏ธ
#MachineLearning #AI #DataScience #TechCommunity #LearningResources #OpenSource
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
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๐ Level up your AI & Data Science skills with HelloEncyclo โ a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
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๐ Use code: PRESALE-BOOK-WAVE-2GFG
๐ https://helloencyclo.com/?ref=HUSSEINSHEIKHO
GitHub
GitHub - dair-ai/ML-Course-Notes: ๐ Sharing machine learning course / lecture notes.
๐ Sharing machine learning course / lecture notes. - dair-ai/ML-Course-Notes
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Join our livestream with Marina Wyss, Senior Applied Scientist at Twitch, as we discuss how to break into AI Engineering in 2026.
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Many people interested in AI Engineering are asking the same questions:
โ Where do I start?
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Sign up for FREE and save your seat here: luma.com/qgz4g4r7
Why should you join?
Many people interested in AI Engineering are asking the same questions:
โ Where do I start?
๐ค Do I need deep math first?
๐ง Should I focus on ML, LLMs, RAG, or AI agents?
๐งญ How do I avoid wasting time learning the wrong things?
๐ How do I go from learning to becoming hireable?
If youโre interested in AI Engineering but unsure how to approach it, this livestream is for you.
What youโll learn
โฆ What AI Engineering really is
โฆ Where beginners should start
โฆ What skills and topics actually matter
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โฆ Self-study vs bootcamp vs MSc
โฆ How to think about becoming hireable in AI
โฆ Practical advice from someone already working in the field
Sign up for FREE and save your seat: luma.com/qgz4g4r7
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5 Fun Papers That Explain LLMs Clearly ๐โจ
Want to understand LLMs better? Start with these five foundational papers that explain how they work. ๐ค
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. ๐ง But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. ๐ This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. ๐ฌ In this article, we will go through five papers that explain how LLMs work. So, let's get started. ๐
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
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Want to understand LLMs better? Start with these five foundational papers that explain how they work. ๐ค
Large language models (LLMs) can feel complicated at first. There are transformers, attention layers, scaling laws, pretraining, instruction tuning, human feedback, retrieval, and many other ideas around them. ๐ง But the best way to understand large language models is not to start with a huge textbook. A better way is to read a few important papers that each explain one major part of the system. ๐ This article is part of a fun series where we learn by exploring core ideas, practical projects, and the research papers behind modern technology. ๐ฌ In this article, we will go through five papers that explain how LLMs work. So, let's get started. ๐
More: https://www.kdnuggets.com/5-fun-papers-that-explain-llms-clearly
#LLM #AI #MachineLearning #DeepLearning #DataScience #Tech
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