Machine Learning
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Real Machine Learning β€” simple, practical, and built on experience.
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Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ Improving Code Quality with Array and DataFrame Type Hints

πŸ—‚ Category:

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 12 min read

How generic specification permits powerful static and runtime validation
πŸ“Œ Shared Nearest Neighbors: A More Robust Distance Metric

πŸ—‚ Category:

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 36 min read

A distance metric that can improve prediction, clustering, and outlier detection in datasets with many…
πŸ“Œ AdEMAMix: A Deep Dive into a New Optimizer for Your Deep Neural Network

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 15 min read

A better and faster option than the ADAM optimizer, from Apple Research
πŸ“Œ The Evolution of Text to Video Models

πŸ—‚ Category: DEEP LEARNING

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 10 min read

Simplifying the neural nets behind Generative Video Diffusion
πŸ“Œ How to Build Your Own Roadmap for a Successful Data Science Career

πŸ—‚ Category: CAREER ADVICE

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 4 min read

Our weekly selection of must-read Editors’ Picks and original features
πŸ“Œ A Closer Look at Scipy’s Stats Module – Part 2

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read

Let’s learn the main methods from scipy.stats module in Python.
πŸ“Œ A Closer Look at Scipy’s Stats module – Part 1

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 7 min read

Let’s learn the main methods from scipy.stats module in Python.
πŸ“Œ Emerging Tech Is Nothing Without Methodology

πŸ—‚ Category: ANALYTICS

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 6 min read

Or: a Hundred Ways to Solve a Complex Problem
πŸ“Œ A Visual Exploration of Semantic Text Chunking

πŸ—‚ Category: NATURAL LANGUAGE PROCESSING

πŸ•’ Date: 2024-09-19 | ⏱️ Read time: 22 min read

Use embeddings and visualization tools to split text into meaningful chunks
πŸ“Œ Principal Component Analysis – Hands-On Tutorial

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-18 | ⏱️ Read time: 13 min read

Dimensionality reduction through Principal Component Analysis (PCA).
πŸ“Œ Uncertainty in Markov Decisions Processes: a Robust Linear Programming approach

πŸ—‚ Category: MATH

πŸ•’ Date: 2024-09-18 | ⏱️ Read time: 8 min read

Theoretical derivation of the Robust Counterpart of Markov Decision Processes (MDPs) as a Linear Program…
Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements

My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward

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πŸ“Œ GPU Accelerated Polars – Intuitively and Exhaustively Explained

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-09-17 | ⏱️ Read time: 16 min read

Fast Dataframes for Big Problems
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πŸ“Œ Polars + NVIDIA GPU Tutorial

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-17 | ⏱️ Read time: 4 min read

Using Polars with NVIDIA GPU can speed up your data pipelines
πŸ“Œ The Math Behind Kernel Density Estimation

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-17 | ⏱️ Read time: 13 min read

Exploring the foundations, concepts, and math of kernel density estimation
πŸ“Œ Model Management with MLflow, Azure, and Docker

πŸ—‚ Category: MACHINE LEARNING

πŸ•’ Date: 2024-09-17 | ⏱️ Read time: 11 min read

A guide to tracking experiments and managing models
πŸ“Œ Football and Geometry – Passing Networks

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-16 | ⏱️ Read time: 12 min read

Analyzing Bayer Leverkusen’s Passing Networks from Last Season
πŸ“Œ PySpark Explained: The InferSchema Problem

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-09-16 | ⏱️ Read time: 10 min read

Think before using this common option when reading large CSV’s
πŸ“Œ Introducing NumPy, Part 4: Doing Math with Arrays

πŸ—‚ Category:

πŸ•’ Date: 2024-09-16 | ⏱️ Read time: 12 min read

Plus reading and writing array data!
πŸ“Œ Disability, Accessibility, and AI

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-09-16 | ⏱️ Read time: 11 min read

A discussion of how AI can help and harm people with disabilities