TensorFlow v2.0 Cheat Sheet
#TensorFlow is an open-source software library for highperformance numerical computation. Its flexible architecture enables to easily deploy computation across a variety of platforms (CPUs, GPUs, and TPUs), as well as mobile and edge devices, desktops, and clusters of servers. TensorFlow comes with strong support for machine learning and deep learning.
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  Machine Learning Glossary
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.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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  The program covers topics of #NLP, #CV, #LLM and the use of technology in medicine, offering a full cycle of training - from theory to practical classes using current versions of libraries.
The course is designed even for beginners: if you know how to take derivatives and multiply matrices, everything else will be explained in the process.
The lectures are released for free on YouTube and the #MIT platform on Mondays, with the first one already available
.
All slides, #code and additional materials can be found at the link provided.
π Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew
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  Numpy @CodeProgrammer.pdf
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  π¨π»βπ» For the past few days, I've been busy preparing this comprehensive tutorial on the NumPy library for data science, trying to cover all the tips and tricks of this library.
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  Python #Pandas Cheat Sheet πΌ 
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  πβοΈTODAY FREEβοΈπ
Entry to our VIP channel is completely free today. Tomorrow it will cost $500! π₯
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Entry to our VIP channel is completely free today. Tomorrow it will cost $500! π₯
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  Keras Cheat Sheet: Neural Networks in Python
#keras #cheatsheet #python #library #programming #guide
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  π Cheat sheets for data science and machine learning
Link: https://sites.google.com/view/datascience-cheat-sheets
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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  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/
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  Deep Learning with Keras :: Cheat sheet
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  Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf
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  Top 100 Machine Learning Interview Questions & Answers Cheatsheet
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  Python Pandas Interview Questions   Answers Cheatsheet.pdf
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  Python Pandas Interview Questions & Answers Cheatsheet
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π12
  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.
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