Forwarded from Machine Learning with Python
👨🏻💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!
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#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Machine Learning with Python
course lecture on building Transformers from first principles:
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0
The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07
https://www.dropbox.com/scl/fi/jhfgy8dnnvy5qq385tnms/lectureattentionneuralnetworks.pdf?rlkey=fddnkonsez76mf8bzider3hrv&dl=0
The #PyTorch notebooks also demonstrate how to implement #Transformers from scratch:
https://github.com/xbresson/CS52422025/tree/main/labslecture07
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Machine Learning with Python
Pandas Introduction to Advanced.pdf
854.8 KB
👨🏻💻 You can't attend a #datascience interview and not be asked about Pandas! But you don't have to memorize all its methods and functions! With this booklet, you'll learn everything you need.
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Machine Learning with Python
Find these FREE AI Courses here 👇
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Machine Learning with Python
Exercises in Machine Learning
This book contains 75+ exercises
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
This book contains 75+ exercises
Download, read, and practice:
arxiv.org/pdf/2206.13446
GitHub Repo: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://xn--r1a.website/CodeProgrammer ✅
👍9
Forwarded from Machine Learning with Python
Linear Algebra
The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.
Completely FREE.
Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf
The 2nd best book on linear algebra with ~1000 practice problems. A MUST for AI & Machine Learning.
Completely FREE.
Download it: https://www.cs.ox.ac.uk/files/12921/book.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://xn--r1a.website/CodeProgrammer✅
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#MachineLearning Systems — Principles and Practices of Engineering Artificially Intelligent Systems: https://mlsysbook.ai/
open-source textbook focuses on how to design and implement AI systems effectively
open-source textbook focuses on how to design and implement AI systems effectively
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
https://xn--r1a.website/DataScienceM✅
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❤5👍3
Forwarded from Machine Learning with Python
This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
https://dafriedman97.github.io/mlbook/content/introduction.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Forwarded from Machine Learning with Python
"Introduction to Probability for Data Science"
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
One of the best books on #Probability. Available FREE.
Download the book:
probability4datascience.com/download.html
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras
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Mathematical theory of Deep Learning:
[Download 282-page PDF. Updated version]:
arxiv.org/abs/2407.18384
#AI #ML #MachineLearning #DeepLearning #Mathematics #DataScience #DataScientist
⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
[Download 282-page PDF. Updated version]:
arxiv.org/abs/2407.18384
#AI #ML #MachineLearning #DeepLearning #Mathematics #DataScience #DataScientist
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Forwarded from Machine Learning with Python
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
❤6
"Calculus: Early Transcendentals" is an excellent free textbook for building a solid foundation in mathematical analysis. 📘
The book is written in a clear and accessible language, while maintaining the necessary mathematical rigor. It contains a large number of examples and problems, making it suitable for both self-study and use in the educational process. 🎓
The textbook covers a wide range of topics, including:
• limits;
• derivatives;
• integrals;
• sequences and series;
• differential equations;
• multivariate analysis.
I consider this book another valuable tool in the arsenal of anyone studying mathematics. 🛠️
If you are a student and want to master or review key topics in mathematical analysis, or a teacher looking for new ideas and alternative explanations, this textbook is definitely worth attention.
https://open.umn.edu/opentextbooks/textbooks/415
https://github.com/antoniolupetti/algebrica
#Calculus #Math #FreeTextbook #StudyGuide #Mathematics #STEM
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The book is written in a clear and accessible language, while maintaining the necessary mathematical rigor. It contains a large number of examples and problems, making it suitable for both self-study and use in the educational process. 🎓
The textbook covers a wide range of topics, including:
• limits;
• derivatives;
• integrals;
• sequences and series;
• differential equations;
• multivariate analysis.
I consider this book another valuable tool in the arsenal of anyone studying mathematics. 🛠️
If you are a student and want to master or review key topics in mathematical analysis, or a teacher looking for new ideas and alternative explanations, this textbook is definitely worth attention.
https://open.umn.edu/opentextbooks/textbooks/415
https://github.com/antoniolupetti/algebrica
#Calculus #Math #FreeTextbook #StudyGuide #Mathematics #STEM
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❤5
🔖 The Legendary MIT Textbook on Mathematics for Computer Science
Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
#ComputerScience #Mathematics #MachineLearning #DataScience #MIT #OpenSource
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❤6
📚 Mathematics without fluff: Three free books for those who want a solid foundation
Three free mathematics books by Allen Hatcher 🧮
If you're looking for a solid foundation in topology, K-theory, and number theory, Allen Hatcher has an excellent free collection. 🎓
1. Algebraic Topology 📐
A classic textbook on algebraic topology. The book was published by Cambridge University Press, but the online version is available for free under an agreement with the publisher. The website offers the complete PDF, chapters individually, corrections, and additional exercises.
🔗 https://pi.math.cornell.edu/~hatcher/AT/AT.pdf
Additionally: Spectral Sequences - a separate, expanded chapter for this book.
🔗 https://pi.math.cornell.edu/~hatcher/AT/ATch5.pdf
2. Vector Bundles & K-Theory 🧶
A concise book about vector bundles, topological K-theory, and characteristic classes. Currently, approximately 120 pages are available online, covering the basics of vector bundles, a portion of K-theory, Bott periodicity, characteristic classes, and the stable J-homomorphism.
🔗 https://pi.math.cornell.edu/~hatcher/VBKT/VB.pdf
3. Topology of Numbers 🔢
An unusual introduction to number theory through geometry and pictures. It focuses heavily on quadratic forms, Farey diagrams, continued fractions, Pell's equation, quadratic reciprocity, and Conway's topograph. A PDF of approximately 350 pages is available for free.
🔗 https://pi.math.cornell.edu/~hatcher/TN/TNbook.pdf
#Mathematics #Topology #FreeBooks #Learning #STEM #Education
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Three free mathematics books by Allen Hatcher 🧮
If you're looking for a solid foundation in topology, K-theory, and number theory, Allen Hatcher has an excellent free collection. 🎓
1. Algebraic Topology 📐
A classic textbook on algebraic topology. The book was published by Cambridge University Press, but the online version is available for free under an agreement with the publisher. The website offers the complete PDF, chapters individually, corrections, and additional exercises.
🔗 https://pi.math.cornell.edu/~hatcher/AT/AT.pdf
Additionally: Spectral Sequences - a separate, expanded chapter for this book.
🔗 https://pi.math.cornell.edu/~hatcher/AT/ATch5.pdf
2. Vector Bundles & K-Theory 🧶
A concise book about vector bundles, topological K-theory, and characteristic classes. Currently, approximately 120 pages are available online, covering the basics of vector bundles, a portion of K-theory, Bott periodicity, characteristic classes, and the stable J-homomorphism.
🔗 https://pi.math.cornell.edu/~hatcher/VBKT/VB.pdf
3. Topology of Numbers 🔢
An unusual introduction to number theory through geometry and pictures. It focuses heavily on quadratic forms, Farey diagrams, continued fractions, Pell's equation, quadratic reciprocity, and Conway's topograph. A PDF of approximately 350 pages is available for free.
🔗 https://pi.math.cornell.edu/~hatcher/TN/TNbook.pdf
#Mathematics #Topology #FreeBooks #Learning #STEM #Education
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❤5
Foundations of Applied Mathematics is a free series of four textbooks created for the applied and computational mathematics program at Brigham Young University. 📚
The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. 🐍
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. 💻
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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❤4