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- ## Understanding Convolutional Neural Networks (CNNs) Through Excel
Deep Learning

Deep learning is often seen as a black box. We know that it learns from…

angela shi

Nov 17

12 min read
- ## Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 2
Programming

In part 1, we showed how we could leverage HTMX to add interactivity to our…

Benjamin Etienne

Nov 17

16 min read

## Latest

- ## Introducing ShaTS: A Shapley-Based Method for Time-Series Models
Data Science

Why you should not explain your time-series data with tabular Shapley methods

Manuel Franco de la Peña

Nov 17

9 min read
- ## The Absolute Beginner’s Guide to Pandas DataFrames
Data Science

Learn how to initialize dataframes from dictionaries, lists, and NumPy arrays

Ibrahim Salami

Nov 17

5 min read
- ## Javascript Fatigue: HTMX is all you need to build ChatGPT — Part 1
Programming

Building a chatbot (almost) without Javascript, only with Python and HTML.

Benjamin Etienne

Nov 17

12 min read
- ## I Built an IOS App in 3 Days with Literally No Prior Swift Knowledge
AI applications

What I learned about vibe coding, AI tools, and getting started as a solopreneur

Soner Yıldırım

Nov 16

6 min read
- ## Stop Worrying about AGI: The Immediate Danger is Reduced General Intelligence (RGI)
Artificial Intelligence

Let’s make conscious and deliberate choices when we use AI.

Shashank Vatedka

Nov 16

7 min read
- ## How to Automate Workflows with AI
Agentic AI

Learn how to take a manual process and optimize it using AI

Eivind Kjosbakken

Nov 15

7 min read
- ## I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
Machine Learning

Image by Pixabay.com

Javier Marin

Nov 15

9 min read
- ## “The success of an AI product depends on how intuitively users can interact with its capabilities”
Artificial Intelligence

Janna Lipenkova on AI strategy, AI products, and how domain knowledge can change the entire…

TDS Editors

Nov 14

8 min read
- ## How to Crack Machine Learning System-Design Interviews
Machine Learning

A comprehensive guide into Meta, Apple, Reddit, Amazon, Google, and Snap ML design interviews

Aliaksei Mikhailiuk

Nov 14

15 min read

See all of the latest

## Editor’s Picks

- ## Music, Lyrics, and Agentic AI: Building a Smart Song Explainer using Python and OpenAI
Large Language Models

This is how to build an AI-powered Song Explainer using Python and OpenAI

Piero Paialunga

Nov 14

10 min read
- ## LLMs Are Randomized Algorithms
Large Language Models

A surprising connection between the newest AI models and a 50-year old academic field

Udayan Kanade

Nov 13

18 min read
- ## Organizing Code, Experiments, and Research for Kaggle Competitions
Project Management

Lessons and tips learned while earning a Kaggle Competition Medal

Ibrahim Habib

Nov 13

21 min read
- ## How to Evaluate Retrieval Quality in RAG Pipelines (Part 3): DCG@k and NDCG@k
Large Language Models

The third and final part for evaluating the retrieval quality of your RAG pipeline with…

Maria Mouschoutzi

Nov 12

8 min read
- ## AI Hype: Don’t Overestimate the Impact of AI
Artificial Intelligence

Targeting moonshots instead of trolleys

Pascal Janetzky

Nov 11

7 min read
- ## Why Storytelling With Data Matters for Business and Data Analysts
Data Science

Data is driving the future of business and here’s how you can be prepared for…

Rashi Desai

Nov 10

7 min read
- ## LLM-Powered Time-Series Analysis
Large Language Models

Part 2: Prompts for Advanced Model Development

Sara Nobrega

Nov 9

9 min read
- ## Power Analysis in Marketing: A Hands-On Introduction
Statistics

Part 1: What is statistical power and how do we compute it?

Sam Arrington

Nov 8

18 min read
- ## Beyond Numbers: How to Humanize Your Data & Analysis
Data Science

The scintillating grid optical illusion is a perfect metaphor for how raw data can mislead…

Michal Szudejko

Nov 7

16 min read

## The Variable Newsletter

- ## TDS Newsletter: How Compelling Data Stories Lead to Better Business Decisions
The Variable

Why data-driven stories remain an evergreen tool for practitioners

TDS Editors

Nov 13

3 min read
- ## TDS Newsletter: The Theory and Practice of Using AI Effectively
The Variable

When we encounter a new technology — say, LLM applications — some of us tend to jump…

TDS Editors

Nov 6

3 min read

## Deep Dives

- ## Robotics with Python: Q-Learning vs Actor-Critic vs Evolutionary Algorithms
Reinforcement Learning

Build a Custom 3D Environment for your RL Robot

Mauro Di Pietro

Nov 13

15 min read
- ## Deploy Your AI Assistant to Monitor and Debug n8n Workflows Using Claude and MCP
Artificial Intelligence

Use Claude AI to monitor, analyse, and troubleshoot your n8n automation workflows through natural conversation.

Samir Saci

Nov 12

19 min read
- ## The Ultimate Guide to Power BI Aggregations
Data Science

Aggregations are one of the most powerful features in Power BI — learn how to leverage…

Nikola Ilic

Nov 12

10 min read
- ## Feature Detection, Part 2: Laplace & Gaussian Operators
Computer Vision

Laplace meets Gaussian — the story of two operators in edge detection

Vyacheslav Efimov

Nov 12

12 min read
- ## Do You Really Need GraphRAG? A Practitioner’s Guide Beyond the Hype
Large Language Models

A perspective on GraphRAG design best practices, challenges and learnings

Partha Sarkar

Nov 11

15 min read
- ## The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or a LLM (Explained with One Example)
Data Science

A practical use case to describe how the data scientist job changed across three generations…

Piero Paialunga

Nov 11

10 min read

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