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
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π13π₯2
π¨π»βπ» One of the most popular GitHub repositories for "learning and using algorithms in Python" is The Algorithms - Python repo with 196K stars.
βοΈ It has a lot of organized and categorized code that you can use to find, read, and run different algorithms. Everything you can think of is here; from simple algorithms like sorting to advanced algorithms for machine learning, artificial intelligence, neural networks, and more.
β
Why should we use it?
π’ For learning: If you're looking to learn algorithms in action, this is great.
π’ For practice: You can take the codes, run them, and modify them to better understand.
π’ For projects : You can even use the codes here in real-life or academic projects.
π’ For interviews: If you're preparing for data science interviews, this is full of practical algorithms.
βπ³οΈβπ The Algorithms - Python
βπ± GitHub-Repos
β
β
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π14β€3
Pandas Introduction to Advanced.pdf
854.8 KB
π¨π»βπ» 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.
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π25π₯2
Find these FREE AI Courses here π
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
https://www.mltut.com/best-resources-to-learn-artificial-intelligence/
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π18π₯3β€1π―1
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
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π12β€1π₯1π1
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
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π12π―5πΎ3
Forwarded from Machine Learning
#MachineLearning Systems β 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
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https://t.me/DataScienceMβ
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π11β€2
Introduction to Machine Learning Class Notes by Huy Nguyen
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
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π11β€2
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 practicesβsuch as feature engineering or balancing response variablesβor 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
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π11β€3π―2
Stanfordβs Machine Learning - by Andrew Ng
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
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π17β€5
"Machine Learning & LLMs for Beginners"
Don't miss these 2 books of 100-pages. Both are #FREE to read.
π The Hundred-Page Machine Learning Book:
themlbook.com/wiki/doku.php
π The Hundred-Page Language Model Book:
thelmbook.com
Don't miss these 2 books of 100-pages. Both are #FREE to read.
themlbook.com/wiki/doku.php
thelmbook.com
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https://t.me/CodeProgrammerπ
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π9
Stanford's "Design and Analysis of Algorithms" Winter 2025
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
Lecture Notes & Slides: https://stanford-cs161.github.io/winter2025/lectures/
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π10β€1
"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
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π18π―3π₯1
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