Machine Learning
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Real Machine Learning β€” simple, practical, and built on experience.
Learn step by step with clear explanations and working code.

Admin: @HusseinSheikho || @Hussein_Sheikho
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πŸ“Œ Pre-Commit & Git Hooks: Automate High Code Quality

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 6 min read

How to improve your code quality with pre-commit and git hooks
πŸ“Œ KernelSHAP can be misleading with correlated predictors

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 7 min read

A concrete case study
πŸ“Œ AI for the Absolute Novice – Intuitively and Exhaustively Explained

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 40 min read

From β€œI’ve never coded” to making an AI model from scratch.
πŸ“Œ LLMOps – Serve a Llama-3 model with BentoML

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 5 min read

Quickly set up LLM APIs with BentoML and Runpod
πŸ“Œ We Need to Raise the Bar for AI Product Managers

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 10 min read

How to Stop Blaming the β€˜Model’ and Start Building Successful AI Products
πŸ“Œ Create Stronger Decision Trees with bootstrapping and genetic algorithms

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-09 | ⏱️ Read time: 31 min read

A technique to better allow decision trees to be used as interpretable models
πŸ“Œ Ask Not What AI Can Do for You – Ask What You Can Achieve with AI

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 11 min read

Unlock AI for Everyone: Discover How You Can Use LLMs in Everyday Tasks
πŸ“Œ 3 Key Tweaks That Will Make Your Matplotlib Charts Publication Ready

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read

Matplotlib charts are an eyesore by default – here’s what to do about it.
πŸ“Œ The Big Questions Shaping AI Today

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read

Our weekly selection of must-read Editors’ Picks and original features
πŸ“Œ 5 Proven Query Translation Techniques To Boost Your RAG Performance

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 11 min read

How to get near-perfect LLM performance even with ambiguous user inputs
πŸ“Œ How to Use Machine Learning to Inform Design Decisions and Make Predictions

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 15 min read

An Introductory Guide and Use Case for Applied Data Science
πŸ“Œ Spatial Interpolation in Python

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-08 | ⏱️ Read time: 4 min read

Using the Inverse Distance Weighting method to infer missing spatial data
πŸ“Œ Reinforcement Learning, Part 6: n-step Bootstrapping

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 7 min read

Pushing the boundaries: generalizing temporal difference algorithms
πŸ“Œ AI Shapeshifters: The Changing Role of the AI Engineer and Applied Data Scientist

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 5 min read

The role of AI Engineer and Applied Data Scientist has undergone a remarkable transformation. Where…
πŸ“Œ Short and Sweet: Enhancing LLM Performance with Constrained Chain-of-Thought

πŸ—‚ Category: ARTIFICIAL INTELLIGENCE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read

Sometimes few words are enough: reducing output length for increasing accuracy
πŸ“Œ High-Performance Data Processing: pandas 2 vs. Polars, a vCPU Perspective

πŸ—‚ Category:

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 8 min read

Polars promises its multithreading capabilities outperform pandas. But is it also the case with a…
πŸ“Œ Strategizing Your Preparation for Machine Learning Interviews

πŸ—‚ Category: CAREER ADVICE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read

Decoding Job Roles and identify focus areas
πŸ“Œ Stop Wasting LLM Tokens

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 5 min read

Batching your inputs together can lead to substantial savings without compromising on performance
πŸ“Œ Create Synthetic Dataset Using Llama 3.1 to Fine-Tune Your LLM

πŸ—‚ Category: DATA SCIENCE

πŸ•’ Date: 2024-08-07 | ⏱️ Read time: 10 min read

Using the giant Llama 3.1 405B and Nvidia Nemotron 4 reward model to create a…
πŸ“Œ Visualising Strava Race Analysis

πŸ—‚ Category:

πŸ•’ Date: 2024-08-06 | ⏱️ Read time: 17 min read

Two New Graphs That Compare Runners on the Same Event