Towards Data Science (@TDataScience)

Towards Data Science @TDataScience

A Medium publication sharing concepts, ideas, and codes. Share your insights and projects with like-minded readers: https://t.co/Mh1ZLme1o4.

Tweets by Towards Data Science

What is Average Precision in Object Detection & Localization Algorithms and how to calculate it? by @_aqeelanwar https://t.co/3CMA1bxtEq

Shared by Towards Data Science at 5/15/2022

Fundamentals of Matrix Algebra with Python | Part 1 by @AndrewDavies0_0 https://t.co/e1gNxNHSpq

Shared by Towards Data Science at 5/15/2022

In a quick tutorial, @AbizerJ explains how to handle DNS and SSL/TLS for your AWS Kubernetes cluster. https://t.co/bMwWE9FevJ

Shared by Towards Data Science at 5/15/2022

What are the advantages of working with Python on browsers? @Thuwarakesh offers his first impressions after giving it a try. https://t.co/Sm6oSF5KNX

Shared by Towards Data Science at 5/15/2022

Divyanshu Raj discusses the benefits of a semi-supervised Transformers architecture — an approach that can help a model achieve high accuracy and robustness. https://t.co/UMxjiI92Bz

Shared by Towards Data Science at 5/15/2022

Spotlighting — a visual approach to precisely interpret the clustering by Pranay Dave https://t.co/hZLikjiUyP

Shared by Towards Data Science at 5/15/2022

How to make a PyTorch Transformer for time series forecasting by Kasper Groes Albin Ludvigsen https://t.co/KlYcrymjXr

Shared by Towards Data Science at 5/15/2022

Generation of a synthetic microbial dataset with deep learning style transfer by Jarosław Pawłowski https://t.co/M905R5MKqB

Shared by Towards Data Science at 5/15/2022

10 Terminal Commands Anyone Learning Python Should Know by @frankandradec https://t.co/XlalksSMw8

Shared by Towards Data Science at 5/14/2022

Stochastic Processes Simulation — Geometric Brownian Motion by Diego Barba https://t.co/S3EhrksNCr

Shared by Towards Data Science at 5/14/2022

Beginner’s Guide to Gradient Descent by Niklas Lang https://t.co/uYly0ZJh4i

Shared by Towards Data Science at 5/14/2022

New preprint describes a novel parameter-free geometric transformer of atomic coordinates to predict biological interfaces in proteins by @labriataphd https://t.co/wPta2EDuXy

Shared by Towards Data Science at 5/14/2022

SSL/TLS for your Kubernetes Cluster with Cert-Manager by @AbizerJ https://t.co/liqDReC88E

Shared by Towards Data Science at 5/14/2022

Looking for patterns in satellite image time series with python by Guilherme M. Iablonovski https://t.co/UVUeXnw4Ps

Shared by Towards Data Science at 5/14/2022

A Framework for Embedding Decision Intelligence into your Organization by Erik Balodis https://t.co/Ntz2aAYO2q

Shared by Towards Data Science at 5/14/2022

Deepfakes, and their ability to spread AI-generated misinformation, have recently emerged as a major concern. Niklas Lang's primer explains how they're created — and how we can better detect them to limit potential harm. https://t.co/kyqZGKIvzk

Shared by Towards Data Science at 5/14/2022

If you'd like to switch from SQL to PySpark for big-data processing, here's a concise tutorial by Michael Berk, where he explains how to make the transition smooth. https://t.co/YR1pM1DUix

Shared by Towards Data Science at 5/14/2022

Stochastic Processes Simulation — The Cox-Ingersoll-Ross Process by Diego Barba https://t.co/3b6qzQH0bN

Shared by Towards Data Science at 5/14/2022

Using Python to Find Outliers With IQR: A How-To Guide by Peter Grant https://t.co/9z60BCJKZf

Shared by Towards Data Science at 5/14/2022

For a clear overview of regularization in linear regression models, here's the latest contribution by Federico Trotta. https://t.co/flf2hBiksN

Shared by Towards Data Science at 5/14/2022

Aaron Zhu discusses the ins and outs of omitted variables, confounding variables, irrelevant variables, and multicollinearity in the context of causal inference and linear regression. https://t.co/vXMIFt6OjY

Shared by Towards Data Science at 5/13/2022

Build an Event-Driven Neural Style Transfer Application Using AWS Lambda by Samhita Alla https://t.co/cgSfprA72f

Shared by Towards Data Science at 5/13/2022

An In-Depth Tutorial to Python Decorators That You Can Actually Use by @BexTuychiev https://t.co/XdFCykXsHY

Shared by Towards Data Science at 5/13/2022

Defining a Taxonomy of Roles in The Data Discipline by @KurtisPykes https://t.co/z06m3njhcj

Shared by Towards Data Science at 5/13/2022

Building a Text Preprocessing Microservice with FastAPI by João Pedro https://t.co/VFcGfUbucd

Shared by Towards Data Science at 5/13/2022

How To Ignore Jupyter Notebook From Github Language Stats? by @ahmed_besbes_ https://t.co/84IwFfw8vD

Shared by Towards Data Science at 5/13/2022

Image Processing: Trivialized by Dhruv Gangwani https://t.co/m14vB6bd31

Shared by Towards Data Science at 5/13/2022

Labeling And Visualizing Images For Object Detection by @SkandaVivek https://t.co/JRdbwYNkax

Shared by Towards Data Science at 5/13/2022

Previewing the soon-to-be-released Python 3.11, Dario Radečić discusses three of its most promising features: better error locations, exception notes, and a built-in TOML parser. https://t.co/ivIaqMdtks

Shared by Towards Data Science at 5/13/2022

Is it possible to estimate the performance of an ML model in the absence of ground truth? @erykml1 looks at how you should approach this problem. https://t.co/yXLay9JOIw

Shared by Towards Data Science at 5/12/2022

For a comprehensive overview of the core steps involved in data preprocessing, here's a helpful guide by Suhas Maddali. https://t.co/y1iZhR5x4h

Shared by Towards Data Science at 5/12/2022

"Cloud GIS can really scale at a global level and provide computational resources to run models that can give insights into spatial data." In our recent Q&A, @BryanRVallejo shared his thoughts on promising future directions for geospatial science. https://t.co/R6oE31vhr8

Shared by Towards Data Science at 5/12/2022

Collaborative Denoising Autoencoders on PyTorch Lightning by @dkmvalerio https://t.co/Ms4oAFc6E7

Shared by Towards Data Science at 5/12/2022

Wouldn't it be neat if you could predict your model's performance without having to wait for results from your control group? @SamueleMazzanti writes about a novel algorithm that can help you save a lot of precious time. https://t.co/noZZGh2Wg7

Shared by Towards Data Science at 5/10/2022

"I overcame my limits when I learned a programming language for geospatial analytics. Python is my armor." @BryanRVallejo reflects on his fascinating, world-crossing journey into geospatial science. https://t.co/Mq68vvhOHv

Shared by Towards Data Science at 5/10/2022

8 Tips To Build Powerful Deep Learning Models for Visual Similarity by @ahmed_besbes_ https://t.co/oG96Orn8yt

Shared by Towards Data Science at 5/10/2022

"As I began writing more and more complex code, list multiplication started to get on my nerves." @ayar_mohammed explains why this neat Python feature should only be used with caution (if at all). https://t.co/P2dhY5nnib

Shared by Towards Data Science at 5/10/2022

When things go awry with your code, you might need an escape hatch. Adrian Causby shares a quick tutorial that shows how to use decorator functions to create an easy exit in your Python app. https://t.co/vFDPVqxU3a

Shared by Towards Data Science at 5/10/2022

Diagnosing Pneumonia from X-Ray Images Using Convolutional Neural Network by Tony Tsoi https://t.co/yf0NPRPpnN

Shared by Towards Data Science at 5/9/2022

Top 3 Ways Your Anomaly Detection Models Can Earn Your Trust by @michaelhoarau https://t.co/L5JsrSqFYe

Shared by Towards Data Science at 5/9/2022

What is well-modeled data for analysis? by @bzdyelnik https://t.co/qAhZGn5UGC

Shared by Towards Data Science at 5/9/2022

Extract knowledge from text: End-to-end information extraction pipeline with SpaCy and Neo4j by Tomaz Bratanic https://t.co/ORsxdQGKOm

Shared by Towards Data Science at 5/9/2022

How to Easily Run Python Visualizations On a Web Browser with PyScript by @frankandradec https://t.co/AuapcINNmM

Shared by Towards Data Science at 5/9/2022

Federico Trotta works on a diabetes dataset to demonstrate the ins and outs of testing different models for prediction tasks. https://t.co/Q7DPJz04lL

Shared by Towards Data Science at 5/9/2022

Looking to learn something new? Here's @MarioDagrada's hands-on introduction to physics-informed neural networks, executed with PyTorch. https://t.co/ND7HsJnZOW

Shared by Towards Data Science at 5/8/2022

Python Libraries for Mesh, Point Cloud, and Data Visualization (Part 1) by Ivan Nikolov https://t.co/x8pp3TPVFx

Shared by Towards Data Science at 5/8/2022

Dynamically Add Arguments to Argparse | Python Patterns by @mattiadgPA https://t.co/XrvdWgwJfI

Shared by Towards Data Science at 5/8/2022

A simple route optimization using Google BigQuery (and Google Maps) by Astandri Koesriputranto https://t.co/TlnbPZey9j

Shared by Towards Data Science at 5/8/2022

Beginners Baseline Model for Machine Learning Project by @CornelliusYW https://t.co/nJNyvTyQPV

Shared by Towards Data Science at 5/8/2022

How Policy Gradients in Reinforcement Learning can get you to the Moon? by @paulabartabajo_ https://t.co/qBswgO6EQP

Shared by Towards Data Science at 5/8/2022

Kaggle hacking: Validate a simple hypothesis against a hidden dataset by @JirkaBorovec https://t.co/kVVjBaMJqb

Shared by Towards Data Science at 5/8/2022

Understanding Neural Network Embeddings by @frankzliu https://t.co/7cxsw8tuEp

Shared by Towards Data Science at 5/8/2022

Why Neural Networks Can Solve Simple Tasks by @ReboucasYgor https://t.co/fFnrWhyNMU

Shared by Towards Data Science at 5/8/2022

Survival Analysis vs. Logistic Regression by @meaganvoulo https://t.co/5HQXrGcTaR

Shared by Towards Data Science at 5/8/2022

Part 12: Matrix Profiles For Machine Learning by Sean Law https://t.co/fFlB222AR8

Shared by Towards Data Science at 5/8/2022

Including superfluous variables in your regression model can have dire consequences on its performance — @TimeReasoning explains why you should avoid this mistake. https://t.co/QscqLCZmoV

Shared by Towards Data Science at 5/7/2022

For a beginner-friendly guide to principal component analysis, head right over to Niklas Lang's new post. https://t.co/ulvF8vj8ed

Shared by Towards Data Science at 5/7/2022

Can we use time series techniques to assess a population's mental health during the first months of the COVID-19 pandemic? Hannah Roos walks us through a full time series analysis in Python, and reflects on the importance of data-quality checks. https://t.co/ASgTRGYkfp

Shared by Towards Data Science at 5/7/2022

Image Analytics for Everyone: Image Embeddings with Orange by @primozG92 https://t.co/zDzPEsn09F

Shared by Towards Data Science at 5/7/2022

Gaussian Process Kernels by Y. Natsume https://t.co/q2tenEnhUf

Shared by Towards Data Science at 5/7/2022

Closed-Form Solution to Linear Regression by @margo_hatcher1 https://t.co/onpmWYepro

Shared by Towards Data Science at 5/6/2022

Functions That Return Functions: Higher-Order Functions and Decorators in Python with Examples by @dvgodoy https://t.co/WRJUCyqJvB

Shared by Towards Data Science at 5/6/2022

Graphs, graphs, and more graphs! Our weekly highlights include excellent articles by Cristiana de Azevedo v.S. on graph-modeling frameworks, @MatteoCourthoud on directed acyclic graphs (DAGs), and @maximelabonne on a promising new architecture for GNNs. https://t.co/pObvk9Ikxy

Shared by Towards Data Science at 5/6/2022

Hierarchical Clustering and K-means Clustering on Country Data by @AndreaGustafsen https://t.co/AbNSiyJGA7

Shared by Towards Data Science at 5/6/2022

"Time and seasonality features are often assumed in time series analysis, ignoring their crucial role as an input in model calibration." Read more from Alvin T. Tan's post below. https://t.co/TwtN88Ja87

Shared by Towards Data Science at 5/6/2022

Autoencoders: From Vanilla to Variational by @MichalOleszak https://t.co/Vaok1HCR0n

Shared by Towards Data Science at 5/6/2022

Named Entity Recognition with BERT in PyTorch by Ruben Winastwan https://t.co/pbibwXixkk

Shared by Towards Data Science at 5/6/2022

Sparse Autoencoder Neural Networks — How to Utilise Sparsity for Robust Information Encoding by @SolClover https://t.co/SFiPlOSCPK

Shared by Towards Data Science at 5/6/2022

If you'd like to learn how to train your neural networks in minutes instead of hours, here's a handy tutorial by Bhaskar Agarwal. https://t.co/ikZFdfLQyz

Shared by Towards Data Science at 5/6/2022

Create a Gradient Descent Algorithm with Regularization from Scratch in Python by Turner Luke https://t.co/Q8mJtH8tAy

Shared by Towards Data Science at 5/6/2022

Hands on Climate Time Series Clustering using Machine Learning, with Python by Piero Paialunga https://t.co/1E3XIpLwcI

Shared by Towards Data Science at 5/5/2022

How to Add New Data to a Pretrained Model in Scikit-learn by @alod83 https://t.co/nhte85BgvG

Shared by Towards Data Science at 5/5/2022

Create a K-Means Clustering Algorithm from Scratch in Python by Turner Luke https://t.co/kcETHDPbHD

Shared by Towards Data Science at 5/5/2022

Principal Component Analysis from the ground up with Python by @RobinThibaut https://t.co/e1PkqkaZou

Shared by Towards Data Science at 5/5/2022

Stochastic Processes Simulation — The Ornstein Uhlenbeck Process by Diego Barba https://t.co/HypgqvJIFG

Shared by Towards Data Science at 5/5/2022

Create Your Own k-Nearest Neighbors Algorithm in Python by Turner Luke https://t.co/Wn8W7EIBhG

Shared by Towards Data Science at 5/5/2022

Relying on publicly available happiness and conflict data, @Lanchuhuong walks us through the process of creating striking geospatial visualizations. https://t.co/JHMIpp8VX5

Shared by Towards Data Science at 5/5/2022

Deep learning meets Hebrew diacritics: Morris Alper introduces us to UNIKUD, the first open-source Hebrew "nakdan" tool, which uses no rule-based logic. https://t.co/TKdLuu47tl

Shared by Towards Data Science at 5/5/2022

Are we getting closer to the point where humans—or the AIs we build—can communicate with animals? @_aitalks_ explores a centuries-old desire through the lens of ML algorithms and the challenges of replicating non-human language. https://t.co/yrChJZEfcB

Shared by Towards Data Science at 5/4/2022

Should you invest some extra time in post-processing the results of your time series anomaly-detection models? @michaelhoarau argues that's where you might find some of the most interesting insights. https://t.co/6WSkkOWQM7

Shared by Towards Data Science at 5/4/2022

10 Must-know Seaborn Functions for Multivariate Data Analysis in Python by Susan Maina https://t.co/wNqY2nmnYz

Shared by Towards Data Science at 5/4/2022

The Basics of Neural Networks (Neural Network Series) — Part 1 by @koech_kiprono1 https://t.co/GnGFEf2S0o

Shared by Towards Data Science at 5/4/2022

Generate distractors for MCQs using Word Vectors, Sentence Transformers and MMR algorithm by @ramsri_goutham https://t.co/qiZ7mSFYPk

Shared by Towards Data Science at 5/4/2022

Optimal Undersampling using Machine Learning, with Python by Piero Paialunga https://t.co/gfOuXyVPML

Shared by Towards Data Science at 5/3/2022

Predicting conversion events from Google Analytics dataset for Google Merchandise store in BigQuery by @ExcelStrategies https://t.co/41UbY8XwRB

Shared by Towards Data Science at 5/3/2022

Probabilistic Machine Learning and Weak Supervision by @hugobowne https://t.co/HYURkROr95

Shared by Towards Data Science at 9/4/2021

Four Deep Learning Papers to Read in September 2021 by @RobertTLange https://t.co/MXiX0I5Oo8

Shared by Towards Data Science at 9/4/2021

This weekend, get into the nuts and bolts of algorithms and models with our latest recommended reads. They include @CJLovesData1 on the FastRP algorithm, @carolinabento on gradient-boosted decision trees, and @Wen_Billiams on his AI gaming project. https://t.co/UcNDNhxvnQ

Shared by Towards Data Science at 9/3/2021

Our September Edition is out! Catch up with Abdullah Farouk, who shares his thoughts (and some reading recommendations) on probabilistic graphical models (PGMs) and their potential to address our complex world. https://t.co/5lEg4hSwGm

Shared by Towards Data Science at 9/2/2021

How can Machine Learning algorithms include better Causality? by @lina_faik https://t.co/wGML4gZWNl

Shared by Towards Data Science at 8/21/2021

"We will never be able to perfectly predict the future, but we can use tools from the statistical field of forecasting to better understand what lies ahead." Don't miss @mgsosna's deep dive into time-series analysis and ARIMA models. https://t.co/AKgC5rXCCW

Shared by Towards Data Science at 8/13/2021

Youness Mansar implements a simple but powerful recommendation system called BERT4Rec to movie recommendations on a database of around 60,000 movies https://t.co/zfXnXsP4cu

Shared by Towards Data Science at 4/30/2021

Debugging your code might not be the most fun part of your day, but the tools that @KhuyenTran16 recommends for tracking and visualizing your Python output can help. https://t.co/Da5sr0pBtT

Shared by Towards Data Science at 4/30/2021

K-Means Clustering — A Comprehensive Guide to Its Successful Use in Python by @SolClover https://t.co/1M6hgb3SMA

Shared by Towards Data Science at 4/30/2021

Deep Learning For Audio With The Speech Commands Dataset by Peter Gao https://t.co/aHyxY0b2Ba

Shared by Towards Data Science at 4/26/2021