"Introduction to Applied Linear Algebra" by S. Boyd (Stanford) & L. Vandenberghe (UCLA)
📘 Freely available at: https://web.stanford.edu/~boyd/vmls/
📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9
📘 Freely available at: https://web.stanford.edu/~boyd/vmls/
📽 Lecture Videos at: https://youtube.com/playlist?list=PLoROMvodv4rMz-WbFQtNUsUElIh2cPmN9
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Best Deep Learning Courses:
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https://mltut.com/best-deep-learning-courses-on-coursera/
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🍃 Stem-Leaf Plot - An intelligent visualization!
It's a simple and effective way to visualize and compare datasets.
Give it a try next time you need to compare datasets!
✍🏽 Have you used stem-leaf plots before?
It's a simple and effective way to visualize and compare datasets.
📊 Imagine we have two datasets: Set 1 (7, 12, 14, 17, 19, 23, 25) and Set 2 (3, 11, 16, 18, 20, 21, 24). We'll use a stem-leaf plot to compare them.
🌿 First, let's create the 'stem' which represents the tens place (0, 1, 2) and the 'leaf' represents the ones place (0-9).
🔍 By comparing the plots, we can see that Dataset 1 has higher values in the tens place, while Dataset 2 has a more uniform distribution.
🎯 Stem-leaf plots are great for small datasets and provide a clear picture of data distribution. The special thing about a stem-and-leaf diagram is that the original data can be read out of the graphical representation.
Give it a try next time you need to compare datasets!
✍🏽 Have you used stem-leaf plots before?
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Create pivot tables in your Jupyter Notebook:
Here's the link to the #GitHub repo and documentation:
https://pivottable.js.org/examples/
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.
#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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Forwarded from Machine Learning with Python
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #IBMDataScience #FreeCourses #Certification #LearnDataScience
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The Hundred-Page Language Models Book
Read it:
https://github.com/aburkov/theLMbook
Read it:
https://github.com/aburkov/theLMbook
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Forwarded from Machine Learning with Python
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https://t.me/CodeProgrammer🖥
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Forwarded from Machine Learning with Python
📁 I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!👇
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💬 Tags: #Datacamp
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Forwarded from ENG. Hussein Sheikho
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
✅ https://t.me/addlist/8_rRW2scgfRhOTc0
✅ https://t.me/codeprogrammer
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NumPy Practical Examples: Useful Techniques
🔰 Master #NumPy by diving into compelling examples. You will read data into an array, analyze structured arrays for reconciliation, efficiently chart your analysis, and transform a custom function into a vectorized function.
Enroll free: https://realpython.com/numpy-example/
✅ https://t.me/addlist/8_rRW2scgfRhOTc0 - Make love ❤️
Enroll free: https://realpython.com/numpy-example/
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A guide exploring agent-enhanced #RAG systems. This repository demonstrates single and multi-agent RAG implementations using #LangGraph, with practical examples for building intelligent applications.
Explore the evolution of RAG
https://github.com/asinghcsu/AgenticRAG-Survey
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In this tutorial, you'll gain an understanding of the principles behind #Polars LazyFrames. You'll also learn why using #LazyFrames is often the preferred option over more traditional #DataFrames
Read: https://realpython.com/polars-lazyframe/
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Forwarded from Machine Learning with Python
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It was a challenge - a marathon 300$ to 30.000$ on trading, together with Lisa!
What is the essence of earning?: "Analyze and open a deal on the exchange, knowing where the currency rate will go. Lisa trades every day and posts signals on her channel for free."
🔹Start: $150
🔹 Goal: $20,000
🔹Period: 1.5 months.
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5 #AI Projects You Can Build This Weekend (with #Python)
Enroll Free: https://towardsdatascience.com/5-ai-projects-you-can-build-this-weekend-with-python-c57724e9c461/
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