Forwarded from Python | Machine Learning | Coding | R
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π Cheat sheets for data science and machine learning
Link: https://sites.google.com/view/datascience-cheat-sheets
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN
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Link: https://sites.google.com/view/datascience-cheat-sheets
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN
https://t.iss.one/CodeProgrammer
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Google Cloud just recently released "The PyTorch developer's guide to JAX fundamentals".
Contains a side-by-side implementation of a training loop in both #Pytorch and #JAX + Flax NNX sor those interested in exploring the JAX world in familiar terms.
Link: https://cloud.google.com/blog/products/ai-machine-learning/guide-to-jax-for-pytorch-developers
https://t.iss.one/DataScienceM
Contains a side-by-side implementation of a training loop in both #Pytorch and #JAX + Flax NNX sor those interested in exploring the JAX world in familiar terms.
Link: https://cloud.google.com/blog/products/ai-machine-learning/guide-to-jax-for-pytorch-developers
https://t.iss.one/DataScienceM
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The Roadmap for Mastering Language Models in 2025
Link: https://machinelearningmastery.com/the-roadmap-for-mastering-language-models-in-2025/
Link: https://machinelearningmastery.com/the-roadmap-for-mastering-language-models-in-2025/
https://t.iss.one/CodeProgrammerβ
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Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf
5.8 MB
Top 100 Machine Learning Interview Questions & Answers Cheatsheet
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #Rο»Ώ
https://t.iss.one/CodeProgrammerβ
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Forwarded from Python | Machine Learning | Coding | R
Machine Learning from Scratch by Danny Friedman
This book is for readers looking to learn new machine learning 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.
π Link: https://dafriedman97.github.io/mlbook/content/introduction.html
This book is for readers looking to learn new machine learning 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.
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://t.iss.one/CodeProgrammerβ
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π¨π»βπ» "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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https://t.iss.one/CodeProgrammerβ
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10 Must-Know Python Libraries for LLMs in 2025
Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.
In this article, we explore 10 of the Python libraries every developer should know in 2025.
Read and learn:
https://machinelearningmastery.com/10-must-know-python-libraries-for-llms-in-2025/
https://t.iss.one/CodeProgrammerβ
Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.
In this article, we explore 10 of the Python libraries every developer should know in 2025.
Read and learn:
https://machinelearningmastery.com/10-must-know-python-libraries-for-llms-in-2025/
https://t.iss.one/CodeProgrammer
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Forwarded from Python | Machine Learning | Coding | R
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
https://t.iss.one/CodeProgrammerβ
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The Beginnerβs Guide to Machine Learning with Rust
In this article, weβll explore the fundamentals of #machinelearning in #Rust, walk through essential libraries, and build a simple machine learning model. Whether youβre a Rust developer curious about machine learning or an machine learning practitioner looking for a high-performance alternative, this article will help you get started.
Link: https://machinelearningmastery.com/the-beginners-guide-to-machine-learning-with-rust/
https://t.iss.one/DataScienceM β€οΈ
In this article, weβll explore the fundamentals of #machinelearning in #Rust, walk through essential libraries, and build a simple machine learning model. Whether youβre a Rust developer curious about machine learning or an machine learning practitioner looking for a high-performance alternative, this article will help you get started.
Link: https://machinelearningmastery.com/the-beginners-guide-to-machine-learning-with-rust/
https://t.iss.one/DataScienceM β€οΈ
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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.
#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://t.iss.one/CodeProgrammerβ
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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/
#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://t.iss.one/CodeProgrammerβ
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Forwarded from Python | Machine Learning | Coding | R
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
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π9
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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://t.iss.one/CodeProgrammerβ
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