Data Science Machine Learning Data Analysis

Create pivot tables in your Jupyter Notebook:

Here's the link to the #GitHub repo and documentation:

https://pivottable.js.org/examples/

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20x faster KMeans with Faiss!!

#KMeans uses a slow, exhaustive search to find the nearest centroids.

#Faiss uses "Inverted Index"—an optimized data structure to store and index data points for approximate neighbor search.

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4.43K viewsedited 07:22

Data Science Machine Learning Data Analysis

Forwarded from Python | Machine Learning | Coding | R

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

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Data Science Machine Learning Data Analysis

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MIT has updated its famous course 6.S191: Introduction to Deep Learning.

.

All slides, #code and additional materials can be found at the link provided.

📌 Fresh lecture : https://youtu.be/alfdI7S6wCY?si=6682DD2LlFwmghew

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Data Science Machine Learning Data Analysis

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Numpy @CodeProgrammer.pdf

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🏳️‍🌈 "NumPy Library" Tutorial

👨🏻‍💻 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.

✅

Why is this booklet different? Because it is not written based on just theoretical concepts, but is the result of my own experiences and learning. It has real and practical examples that will help you better understand #NumPy concepts and use them in your projects.💯

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Data Science Machine Learning Data Analysis

👩‍💻 Prompt Engineering: A Practical Example

This real-world project tutorial covers zero-shot and few-shot prompting, delimiters, numbered steps, role prompts, chain-of-thought prompting, and more. Improve your LLM-assisted projects today.

Link: https://realpython.com/practical-prompt-engineering/

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Data Science Machine Learning Data Analysis

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Keras Cheat Sheet: Neural Networks in Python

#keras #cheatsheet #python #library #programming #guide

https://yangx.top/CodeProgrammer

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Data Science Machine Learning Data Analysis

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Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf

5.8 MB

Top 100 Machine Learning Interview Questions & Answers Cheatsheet

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Data Science Machine Learning Data Analysis

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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 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.

🌟 Link: https://dafriedman97.github.io/mlbook/content/introduction.html

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Data Science Machine Learning Data Analysis

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🔖 The book that paved the way for me to "data science"!

👨🏻‍💻 "Where do I start now?" This was the first and biggest question I faced when I started my Data Science learning journey!

⏪ I was really overwhelmed by the large number of scattered sources, long courses, and specialized books full of heavy terminology. I didn't know how to start and move forward in this direction...

✔️ But the book Intro to Data Science with Python changed everything for me and gave me a new perspective!

✏️ This book is a complete guide to starting from scratch and is great for both beginners and professionals in this field!! From coding with Python to working with data, visualization, and even AI tools, it explains everything in the simplest and most practical way possible.

💸 A great start for anyone looking to learn data science with Python!👇

┌ 🏳️‍🌈 Intro to Data Science with Python
├ 📄 E-book
└ 🐱 GitHub-Repos

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Data Science Machine Learning Data Analysis

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

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Data Science Machine Learning Data Analysis

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Pandas Introduction to Advanced.pdf

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"Pandas Introduction to Advanced" booklet

👨🏻‍💻 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.

✔️ One of the most useful and interesting combinations is using #Pandas with #AWS Lambda, which can be very useful in real projects.

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Data Science Machine Learning Data Analysis

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Data Science Machine Learning Data Analysis

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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

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Data Science Machine Learning Data Analysis

Forwarded from Python | Machine Learning | Coding | R

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

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Data Science Machine Learning Data Analysis

#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

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9.17K viewsedited 06:47

Data Science Machine Learning Data Analysis

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🔗 Machine Learning from Scratch by Danny Friedman

https://dafriedman97.github.io/mlbook/content/introduction.html

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Data Science Machine Learning Data Analysis

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"Introduction to Probability for Data Science"

One of the best books on #Probability. Available FREE.

Download the book:
probability4datascience.com/download.html

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Data Science Machine Learning Data Analysis

✨ Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) ✨

📖 Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive into…...

🏷️ #AdversarialLearning #DeepLearning #ImageProcessing #Keras #MachineLearning #NeuralNetworks #NeuralStructuredLearning #TensorFlow #Tutorial

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Data Science Machine Learning Data Analysis

✨ CycleGAN: Unpaired Image-to-Image Translation (Part 1) ✨

📖 Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 1) Introduction Unpaired Image Translation CycleGAN Pipeline and Training Loss Formulation Adversarial Loss Cycle Consistency Summary Citation Information CycleGAN: Unpaired Image-to-Image Translation (Part 1) In this tutorial, yo...

🏷️ #ComputerVision #CycleGAN #DeepLearning #Keras #KerasandTensorFlow #TensorFlow #UnpairedImageTranslation

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