Towards Data Science
@TDataScience
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The world's leading publication for data science and artificial intelligence professionals. Submit an Article ✍️ https://t.co/57pIMegK1o
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Joined October 2016
.@BENDIMERADSabr1 shares her perspective as a 10-year AI engineer, exploring whether pursuing a career in data science in 2026 is still a worthwhile choice. https://t.co/CHPJ8vRc4z
towardsdatascience.com
An honest view from a 10-year AI Engineer
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Struggling to work on a main project while also handling incoming bug reports? @EivindKjos' guide explains how to use parallel coding agents to manage multiple tasks at once. https://t.co/WRqQgR7cAA
towardsdatascience.com
Learn how to increase LLM usage to achieve increased productivity
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See how Multi-Token Prediction is used in production models. @SteadySurdom covers its successful implementation in @deepseek_ai-V3, validating MTP as a path to more efficient and capable LLMs. https://t.co/SrknUMUHiO
towardsdatascience.com
The simple shift in training that unlocks foresight, faster inference, and better reasoning.
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Save an estimated 10 hours per week of manual investigation. Yassin Zehar shares an automation workflow that detects anomalies and provides context for faster resolution. https://t.co/7w2HltXo4q
towardsdatascience.com
How product, growth and engineering teams can converge on a single signal for better incident management
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Discover the joy of building robust web applications without the friction of a heavy frontend stack. By Benjamin Etienne https://t.co/OtJ1XQ9Fv9
towardsdatascience.com
In part 1, we showed how we could leverage HTMX to add interactivity to our HTML elements. In other words, Javascript without Javascript. To illustrate that, we began building a simple chat that...
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Understand the key difference between Linear and Quadratic Discriminant Analysis. Angela Shi uses Excel to visualize how their decision boundaries are formed. https://t.co/WUxVQdnP4b
towardsdatascience.com
From local distance to global probability
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Bridge the gap between your model and your stakeholders. Shuai Guo teaches you how to build an interactive prototype, allowing users to explore the model's performance directly. https://t.co/PYpc9YHNaN
towardsdatascience.com
With concrete examples of using AI Studio Build mode to learn faster, prototype smarter, communicate clearer, and automate quicker.
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Meet Aakash Goswami! 👋 Our new author takes us behind the scenes of India's RISAT (Radar Imaging Satellite) program. Submit your article today ➡️ https://t.co/mq7C3WigsP
https://t.co/QxGG6sBzik
towardsdatascience.com
The high-resolution physics turning microwave echoes into real-time flood intelligence
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Sometimes you need to find the actual numbers in a subset sum, not just know if a solution exists. Tigran Hayrapetyan shows how his interval-based algorithm can restore the subset of values. https://t.co/lWJjEgcCRB
towardsdatascience.com
An optimal solution to the well-known NP-complete problem, when the input values are close enough to each other.
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🧑🎨 Generate your own Damien Hirst-inspired spot paintings using Python. Mahnoor Javed shares a step-by-step guide to creating generative art with the turtle and colorgram libraries. https://t.co/U1tIg95lpF
towardsdatascience.com
Using Python to generate art
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Build a comprehensive and practical toolkit for time series anomaly detection with Python. Piero Paialunga covers 4 types of anomalies to create a holistic monitoring solution. https://t.co/ypd5EtpOAj
towardsdatascience.com
Here's how to detect point anomalies within each series, and identify anomalous signals across the whole bank
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As LLM-powered agents grow more complex, traditional monitoring falls short. Partha Sarkar shares this guide on implementing production-grade observability. https://t.co/umbwfMxt19
towardsdatascience.com
LLM-as-a-Judge, regression testing, and end-to-end traceability of multi-agent LLM systems
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Implement features that once took hours in just minutes. Learn the agentic coding techniques @EivindKjos uses to reduce development time for new features and bug fixes. https://t.co/xb7LNYeJsS
towardsdatascience.com
Learn how to be an effective engineer with coding agents
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Wondering how to break into AI in 2026? @BENDIMERADSabr1 explains a practical path with usable projects that actually build skills. https://t.co/bwBcn0u5kf
towardsdatascience.com
How to learn AI in 2026 through real, usable projects
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.@BENDIMERADSabr1 breaks down Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI, making sense of the AI landscape in 2026. https://t.co/r2cUnRvBwp
towardsdatascience.com
Understanding AI in 2026 — from machine learning to generative models
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Learn to create graphical outputs using Python. Mahnoor Javed shares a step-by-step tutorial on using the Turtle module to draw shapes and visual patterns, perfect for beginners. https://t.co/KbLeCmVtbz
towardsdatascience.com
A step-by-step tutorial that explores the Python Turtle Module
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Build more effective multi-agent systems by mastering agent handoffs. Kenneth Leung teaches you 2 distinct methods in @LangChainAI #LangGraph for routing tasks. https://t.co/Kq5eli5C7U
towardsdatascience.com
Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph
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Demystify gradient descent with a clear, hands-on example. See how this optimization algorithm works to find a model's optimal parameters in this article by Angela Shi. https://t.co/HzZd60BbGt
towardsdatascience.com
Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss functions, optimization, gradients, scaling, and interpretation. In this article, we rebuild Linear...
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Learn if a data science career is still a valuable path in 2026. @BENDIMERADSabr1 shares a 10-year AI engineer's perspective on the changing market and what skills you need to succeed. https://t.co/CHPJ8vRc4z
towardsdatascience.com
An honest view from a 10-year AI Engineer
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✋ Stop writing slow loops. ✋ This article by @BenjaminNweke11 teaches you how to replace them with vectorized operations for a massive performance boost in Pandas. https://t.co/9q5O9TtW7G
towardsdatascience.com
What I've learned about making Pandas faster after too many slow notebooks and frozen sessions
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