Forwarded from Machine Learning
📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.
It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.
Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.
In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨
https://link.springer.com/book/10.1007/978-981-92-0682-7
#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks
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It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.
Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.
In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨
https://link.springer.com/book/10.1007/978-981-92-0682-7
#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks
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❤7
🚨 Cambridge has just released a real bombshell this time.
📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
From simple to complex.
1️⃣ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2️⃣ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
🔗 https://mml-book.github.io/book/mml-book.pdf
3️⃣ Mathematical Analysis of Machine Learning Algorithms
A more in-depth look at the mathematical principles of machine learning algorithms.
🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf
4️⃣ Theoretical Principles of Deep Learning
The theoretical foundations of deep learning and an understanding of why it all works.
🔗 https://arxiv.org/pdf/2106.10165
5️⃣ Neural Networks and Learning Machines
A systematic analysis of neural networks and the principles of their training.
🔗 https://arxiv.org/pdf/1901.05639
6️⃣ Graph Deep Learning
A good starting point for those who want to understand graph neural networks.
🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf
7️⃣ Machine Learning: A Probabilistic Perspective
It allows you to look at machine learning from a probabilistic and algorithmic perspective.
🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
8️⃣ Probability Theory: Theory and Examples
Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9️⃣ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
🔟 Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech
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📚 A whole collection of classic textbooks on AI and machine learning is now available for free in PDF format.
If you want to really understand machine learning and don't want to waste money on overpriced courses, these ten books will be enough to build a very solid foundation.
From simple to complex.
1️⃣ Understanding Machine Learning
One of the best books for beginners. It covers the basic theoretical algorithms of machine learning.
🔗 https://cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
2️⃣ Mathematical Foundations of Machine Learning
If you're not very confident in your math skills, I would start here.
🔗 https://mml-book.github.io/book/mml-book.pdf
3️⃣ Mathematical Analysis of Machine Learning Algorithms
A more in-depth look at the mathematical principles of machine learning algorithms.
🔗 https://tongzhang-ml.org/lt-book/lt-book.pdf
4️⃣ Theoretical Principles of Deep Learning
The theoretical foundations of deep learning and an understanding of why it all works.
🔗 https://arxiv.org/pdf/2106.10165
5️⃣ Neural Networks and Learning Machines
A systematic analysis of neural networks and the principles of their training.
🔗 https://arxiv.org/pdf/1901.05639
6️⃣ Graph Deep Learning
A good starting point for those who want to understand graph neural networks.
🔗 https://yaoma24.github.io/dlg_book/dlg_book.pdf
7️⃣ Machine Learning: A Probabilistic Perspective
It allows you to look at machine learning from a probabilistic and algorithmic perspective.
🔗 https://people.csail.mit.edu/moitra/docs/bookexv2.pdf
8️⃣ Probability Theory: Theory and Examples
Fundamental theory of probability. Very useful if you want to understand machine learning beyond the level of using ready-made libraries.
🔗 https://sites.math.duke.edu/~rtd/PTE/PTE5_011119.pdf
9️⃣ Fundamentals of Applied Probability
More focus on the practical application of probability theory.
🔗 https://sites.math.duke.edu/~rtd/EP4A/EP4A_April2021.pdf
🔟 Advanced Data Analysis
An advanced level for those who want to seriously improve their data analysis skills.
🔗 https://stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf
#AI #MachineLearning #FreeBooks #DataScience #DeepLearning #Tech
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❤10👍7
Forwarded from Machine Learning
📚 This is probably one of the best technical books on how large language models are trained at scale:
> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. 📖
You can also read it for free on Hugging Face:
https://huggingface.co/spaces/nanotron/ultrascale-playbook
#LLM #AI #MachineLearning #TechBooks #DataScience #Coding
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> GPU memory and profiling
> Breaking down computations into blocks, kernel fusion, and FlashAttention
> Data parallelism, tensor parallelism, pipeline parallelism, and context parallelism
I've already read the free online version, but I still had to buy a physical copy for my library. 📖
You can also read it for free on Hugging Face:
https://huggingface.co/spaces/nanotron/ultrascale-playbook
#LLM #AI #MachineLearning #TechBooks #DataScience #Coding
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❤8🏆2
📚 Download 10 Python books for FREE:
1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65
#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding
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1. Think Python — O’Reilly
https://t.co/X7y3pX68IW
2. The Big Book of Small Python Projects
https://inventwithpython.com/bigbookpython/
3. Data Structures
https://opendatastructures.org/ods-python.pdf
4. Data Science Handbook
https://t.co/9aOLdiAbim
5. Data Analysis
https://wesmckinney.com/book/
6. Data Science
https://t.co/TusyBWGfrx
7. Machine Learning
https://t.co/7veOwESe8q
8. Statistics
https://t.co/waWDQylQpY
9. Think Bayes
https://allendowney.github.io/ThinkBayes2/
10. Clean Code in Python — Packt
https://t.co/OHhVgF1L65
#Python #FreeBooks #Programming #DataScience #MachineLearning #Coding
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤8
Forwarded from Machine Learning
🔖 A Comprehensive Resource for Machine Learning Practice
The platform offers:
🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
🫡 Projects using GPT, RL, and CUDA.
Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓️ Link to the platform
https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
The platform offers:
🫡 1200+ machine learning tasks;
🫡 Mathematics, from linear algebra to PCA;
🫡 Deep Learning, NLP, and Computer Vision;
🫡 Labs with real datasets;
🫡 Learning paths and interview preparation;
🫡 Projects using GPT, RL, and CUDA.
Almost everything can be accessed directly in your browser, and basic tasks are available for free.
⛓️ Link to the platform
https://enghusseinsheikho.substack.com/p/a-comprehensive-resource-for-machine?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#MachineLearning #DeepLearning #DataScience #AI #Coding #Tech
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❤8
Forwarded from Learn Python Coding
😰 15-Hour "Python for Data Science" Course
⚡️ Learn Python for Data Science
https://enghusseinsheikho.substack.com/p/15-hour-python-for-data-science-course?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#Python #DataScience #Coding #Tech #Learning #FreeCourse
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⚡️ Learn Python for Data Science
https://enghusseinsheikho.substack.com/p/15-hour-python-for-data-science-course?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
#Python #DataScience #Coding #Tech #Learning #FreeCourse
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❤7👍3👎1
💾 Free Deep Learning Course from NASA
We found a practical course on applying modern AI methods in astronomy and astrophysics.
It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.
⛓ Link to the course
https://deeplearning4astro.com/
#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience
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We found a practical course on applying modern AI methods in astronomy and astrophysics.
It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.
⛓ Link to the course
https://deeplearning4astro.com/
#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience
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51❤9🔥2
🗂 500+ Cheat Sheets on Machine Learning
This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
⛓ You can find it here
https://github.com/gurovic/MLCheatSheets
#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI
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This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
⛓ You can find it here
https://github.com/gurovic/MLCheatSheets
#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI
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👍8❤2