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TOWARDS DATA SCIENCE


YOUR HOME FOR DATA SCIENCE. A MEDIUM PUBLICATION SHARING CONCEPTS, IDEAS AND
CODES.


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The Hidden World of (Vector) Indexes
THE HIDDEN WORLD OF (VECTOR) INDEXES

Everything you always wanted to know about (vector) indexes but were afraid
to ask.
Olivier Ruas
Nov 15
Efficient Coding in Data Science: Easy Debugging of Pandas Chained Operations
EFFICIENT CODING IN DATA SCIENCE: EASY DEBUGGING OF PANDAS CHAINED OPERATIONS

How to inspect Pandas data frames in chained operations without breaking the
chain into separate statements
Marcin Kozak
Nov 14
Latest
Detecting Generative AI Content
DETECTING GENERATIVE AI CONTENT

On deepfakes, authenticity, and the President’s Executive Order on AI - where
are we headed with detecting AI generated content?
Stephanie Kirmer
Nov 14
Graph Data Science for Tabular Data
GRAPH DATA SCIENCE FOR TABULAR DATA

Graph methods are more general than you may think
Andrew Skabar, PhD
Nov 14
All you need to know to Develop using Large Language Models
ALL YOU NEED TO KNOW TO DEVELOP USING LARGE LANGUAGE MODELS

Explaining in simple terms the core technologies required to start developing
LLM-based applications.
Sergei Savvov
Nov 14
The Hardest Part: Defining A Target For Classification
THE HARDEST PART: DEFINING A TARGET FOR CLASSIFICATION

It isn’t labeled ‘Target_Variable’ in your Production Database!
Chris Bruehl
Nov 14
Modern Semantic Search for Images
MODERN SEMANTIC SEARCH FOR IMAGES

A how-to article leveraging Python, Pinecone, Hugging Face, and the Open AI CLIP
model to create a semantic search application for your…
Josh Poduska
Nov 14
How Self-RAG Could Revolutionize Industrial LLMs
HOW SELF-RAG COULD REVOLUTIONIZE INDUSTRIAL LLMS

Let’s face it — vanilla RAG is pretty dumb. There’s no guarantee responses
returned are relevant. Learn how Self-RAG can significantly help
Skanda Vivek
Nov 14
An Introduction To Deep Learning For Sequential Data
AN INTRODUCTION TO DEEP LEARNING FOR SEQUENTIAL DATA

Highlighting the similarities between time series and NLP
Donato Riccio
Nov 14
Domain Adaptation of A Large Language Model
DOMAIN ADAPTATION OF A LARGE LANGUAGE MODEL

Adapt a pre-trained model to a new domain using HuggingFace
Mina Ghashami
Nov 14
Towards Understanding the Mixtures of Experts Model
TOWARDS UNDERSTANDING THE MIXTURES OF EXPERTS MODEL

New research reveals what happens under the hood when we train MoE models
Samuel Flender
Nov 14
Editors' Picks
TSMixer: The Latest Forecasting Model by Google
TSMIXER: THE LATEST FORECASTING MODEL BY GOOGLE

Explore the architecture of TSMixer and implement it in Python for a
long-horizon multivariate forecasting task
Marco Peixeiro
Nov 14
Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation
RETRIEVAL-AUGMENTED GENERATION (RAG): FROM THEORY TO LANGCHAIN IMPLEMENTATION

From the theory of the original academic paper to its Python implementation with
OpenAI, Weaviate, and LangChain
Leonie Monigatti
Nov 14
Which Quantization Method is Right for You? (GPTQ vs. GGUF vs. AWQ)
WHICH QUANTIZATION METHOD IS RIGHT FOR YOU? (GPTQ VS. GGUF VS. AWQ)

Exploring Pre-Quantized Large Language Models
Maarten Grootendorst
Nov 14
The Graph Coloring Problem: Exact and Heuristic Solutions
THE GRAPH COLORING PROBLEM: EXACT AND HEURISTIC SOLUTIONS

Exploring the classical discrete optimization problem through custom
constructive heuristics and integer programming in Python
Bruno Scalia C. F. Leite
Nov 13
Understanding Instrumental Variables
UNDERSTANDING INSTRUMENTAL VARIABLES

How to estimate causal effects when you cannot randomize treatment
Matteo Courthoud
Nov 13
Chat with Your Dataset using Bayesian Inferences.
CHAT WITH YOUR DATASET USING BAYESIAN INFERENCES.

The ability to ask questions to your data set has always been an intriguing
prospect. You will be surprised how easy it is to learn a local…
Erdogan Taskesen
Nov 13
Features
Take the Next Step to Expand Your Data Science Skill Set
TAKE THE NEXT STEP TO EXPAND YOUR DATA SCIENCE SKILL SET

Our weekly selection of must-read Editors’ Picks and original features
TDS Editors
Nov 9
Had Your Treats? Time for Data Science Tricks
HAD YOUR TREATS? TIME FOR DATA SCIENCE TRICKS

Our weekly selection of must-read Editors’ Picks and original features
TDS Editors
Nov 2
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Deep Dives
Unlocking the Power of Big Data: The Fascinating World of Graph Learning
UNLOCKING THE POWER OF BIG DATA: THE FASCINATING WORLD OF GRAPH LEARNING

Harnessing Deep Learning to Transform Untapped Data into a Strategic Asset for
Long-Term Competitiveness.
Mathieu Laversin
Nov 9
Exposing the Power of the Kalman Filter
EXPOSING THE POWER OF THE KALMAN FILTER

As a data scientist we are occasionally faced with situations where we need to
model a trend to predict future values. Whilst there is a…
Jimmy Weaver
Nov 7
Create your Vision Chat Assistant with LLaVA
CREATE YOUR VISION CHAT ASSISTANT WITH LLAVA

Get started with multimodal conversational models using the open-source
LLaVA model.
Gabriele Sgroi
Nov 11
Demystifying Dependence and Why it is Important in Causal Inference and Causal
Validation
DEMYSTIFYING DEPENDENCE AND WHY IT IS IMPORTANT IN CAUSAL INFERENCE AND CAUSAL
VALIDATION

A step-by-step guide in understanding the concept of dependence and how to apply
it to validate directed acyclic graphs in causal inference
Graham Harrison
Nov 11
Customer Lifetime Value Prediction with PyMC-Marketing
CUSTOMER LIFETIME VALUE PREDICTION WITH PYMC-MARKETING

Explore the Depths of Buy-till-You-Die (BTYD) Modeling and Practical Coding
Techniques
Hajime Takeda
Nov 10
AI Coding: Is Google Bard a Good Python Developer?
AI CODING: IS GOOGLE BARD A GOOD PYTHON DEVELOPER?

How does Google Bard handle Python coding tasks?
Marcin Kozak
Nov 9
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