📌 Visualization of Data with Pie Charts in Matplotlib

🗂 Category:

🕒 Date: 2024-10-16 | ⏱️ Read time: 5 min read

Examples of how to create different types of pie charts using Matplotlib to visualize the…

📌 A Novel Approach to Detect Coordinated Attacks Using Clustering

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-10-16 | ⏱️ Read time: 18 min read

Unveiling hidden patterns: grouping malicious behavior

📌 Exploring DRESS Kit V2

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read

Exploring new features and notable changes in the latest version of the DRESS Kit

📌 The Science Behind AI’s First Nobel Prize

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-10-16 | ⏱️ Read time: 13 min read

How Physics and Machine Learning Joined Forces to Win Physics Nobel 2024

📌 Marketing Mix Modeling (MMM): How to Avoid Biased Channel Estimates

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-16 | ⏱️ Read time: 16 min read

Learn which variables you should and should not take into account in your model.

📌 Beyond Naive RAG: Advanced Techniques for Building Smarter and Reliable AI Systems

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2024-10-16 | ⏱️ Read time: 32 min read

A deep dive into advanced indexing, pre-retrieval, retrieval, and post-retrieval techniques to enhance RAG performance

📌 Will Your Vote Decide the Next President?

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-15 | ⏱️ Read time: 22 min read

Simulating the probability that your singular vote swings the election in November

📌 Normalized Discounted Cumulative Gain (NDCG) – The Ultimate Ranking Metric

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-15 | ⏱️ Read time: 10 min read

NDCG – The Rank-Aware Metric for Evaluating Recommendation Systems

📌 Continual Learning: A Primer

🗂 Category: DEEP LEARNING

🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min read

Plus paper recommendations

📌 I Fine-Tuned the Tiny Llama 3.2 1B to Replace GPT-4o

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-15 | ⏱️ Read time: 8 min read

Is the fine-tuning effort worth more than few-shot prompting?

📌 Dataflow architecture

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-10-15 | ⏱️ Read time: 23 min read

on derived data views and eventual consistency

📌 I Built An AI Human-Level Game Player

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-15 | ⏱️ Read time: 13 min read

Old-school game trees can be incredibly effective.

📌 AI Feels Easier Than Ever, But Is It Really?

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-15 | ⏱️ Read time: 9 min read

The 4 big challenges of building AI products

📌 Evaluating synthetic data

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-10-14 | ⏱️ Read time: 9 min read

Assessing plausibility and usefulness of data we generated from real data

📌 How to Choose the Best ML Deployment Strategy: Cloud vs. Edge

🗂 Category:

🕒 Date: 2024-10-14 | ⏱️ Read time: 17 min read

The choice between cloud and edge deployment could make or break your project

📌 Florence-2: Advancing Multiple Vision Tasks with a Single VLM Model

🗂 Category:

🕒 Date: 2024-10-14 | ⏱️ Read time: 8 min read

A Guided Exploration of Florence-2’s Zero-Shot Capabilities: Captioning, Object Detection, Segmentation and OCR.

📌 PyTorch Optimizers Aren’t Fast Enough. Try These Instead

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-14 | ⏱️ Read time: 12 min read

These 4 advanced optimizers will open your mind.

📌 How to Set Bid Guardrails in PPC Marketing

🗂 Category: DATA SCIENCE

🕒 Date: 2024-10-14 | ⏱️ Read time: 14 min read

Without controls, bidding algorithms can be quite volatile. Learn how to protect performance through adding…

📌 Product-Oriented ML: A Guide for Data Scientists

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-10-14 | ⏱️ Read time: 30 min read

How to build ML products users love

📌 lintsampler: a new way to quickly get random samples from any distribution

🗂 Category: PROBABILITY

🕒 Date: 2024-10-14 | ⏱️ Read time: 5 min read

lintsampler is a pure Python package that can easily and efficiently generate random samples from…

📌 Bringing Structure to Your Data

🗂 Category:

🕒 Date: 2024-10-14 | ⏱️ Read time: 13 min read

Testing assumptions with path models