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  GANs clearly explained with visuals
This website provides a clear explanation, Try it out yourself: poloclub.github.io/ganlab/
π  Tags: #DataScience #Python #ML #AI #LLM #Courses #Pandas #DV #GAN
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  π8π₯1
  Start with Python, explore scikit-learn, and neural networks using PyTorch. Perfect for beginnersβget the skills you need to advance your career in just a few hours.
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  π10
  pandas Project: Make a Gradebook With Python & pandas
Link: https://realpython.com/pandas-project-gradebook/
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  A visual deep dive into Transformers (LLMs)
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  The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
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  Best Data Analyst Online Certifications!
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  π9
  The Hundred-Page Language Models Book
Read it:
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Read it:
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π7
  Generative AI for beginners by Microsoft
21 Lessons teaching everything you need to know to start building Generative AI applications
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  Pen and Paper Exercises in MachineLearning
Free 211-page PDF: arxiv.org/abs/2206.13446
GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
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  π Cheat sheets for data science and machine learning
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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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  π―14π7π₯1π1
  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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  π12π₯3β€2
  ML Tools GRadio.pdf
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  Gradio: The easiest way to demo your models.
- Core Idea: Quickly turn #ML models into interactive web apps.
- No frontend skills needed. It's all #Python.
- Works with any Python code, including custom functions.
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If you're looking to create interactive demos for your ML project, check out #Gradio!
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- Core Idea: Quickly turn #ML models into interactive web apps.
- No frontend skills needed. It's all #Python.
- Works with any Python code, including custom functions.
- Share via temporary links or deploy on #HuggingFace Spaces.
- Get user feedback to improve your models.
If you're looking to create interactive demos for your ML project, check out #Gradio!
β»οΈ Repost if you found this useful
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