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Tinz Twins | AI and Coding

@tinztwinshub.bsky.social
115 followers 45 following 959 posts

Data Scientists | We write about Agentic AI, Data Science, and Software Engineering. 👉🏽 FREE ML cheat sheets: tinztwinshub.com/blog

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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 29/09/2026
If you want to become an AI Engineer, we would follow this roadmap. 10 steps. No fluff. Just pure learning that gets you ahead.
ML/AI Engineer Roadmap listing ten sequential study milestones: 1) Python fundamentals; 2) math & statistics for ML; 3) machine learning algorithms; 4) deep learning; 5) NLP; 6) LLM architectures; 7) fine‑tuning; 8) vector DBs/RAG; 9) agent protocols; 10) agentic AI systems.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 28/09/2026
How does an autoencoder work? 🧐 Autoencoders belong to the semi-supervised methods because you train them only with the normal state of the data.  The network consists of two sections: an encoder function z = g(x) and a decoder function x′ = f (z)
Diagram explaining an autoencoder: input x is compressed by encoder g(x) into a latent z, then reconstructed by decoder f(z) to produce x', illustrating encoding (compression), decoding (reconstruction), and semi‑supervised use.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 29/09/2026
If you want to become an AI Engineer, we would follow this roadmap. 10 steps. No fluff. Just pure learning that gets you ahead.
ML/AI Engineer Roadmap listing ten sequential study milestones: 1) Python fundamentals; 2) math & statistics for ML; 3) machine learning algorithms; 4) deep learning; 5) NLP; 6) LLM architectures; 7) fine‑tuning; 8) vector DBs/RAG; 9) agent protocols; 10) agentic AI systems.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 28/09/2026
How does an autoencoder work? 🧐 Autoencoders belong to the semi-supervised methods because you train them only with the normal state of the data.  The network consists of two sections: an encoder function z = g(x) and a decoder function x′ = f (z)
Diagram explaining an autoencoder: input x is compressed by encoder g(x) into a latent z, then reconstructed by decoder f(z) to produce x', illustrating encoding (compression), decoding (reconstruction), and semi‑supervised use.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 09/08/2026
Precision-Recall Plot: Clearly Explained 👇🏽
Side-by-side precision–recall plots for P:N = 1:1 and 1:3 illustrating a perfect classifier hugging the top and right, a dotted random baseline at precision equal to prevalence (0.5 vs 0.25), regions labeled GOOD/POOR, and that AUC close to 1 is better.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 07/08/2026
What is the Bias-Variance Tradeoff?  Many aspiring data scientists don't understand what bias and variance are and why it is a tradeoff. Here's an easy-to-understand explanation for you. 👇🏽
Diagram showing the bias–variance tradeoff with four bullseye targets: low bias/low variance hits clustered at center, high bias/low variance hits clustered off-center, low bias/high variance hits widely scattered around center, and high bias/high variance hits scattered and off-center; axes
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 08/08/2026
What's the difference between accuracy and precision?
Four bullseye targets on an accuracy (vertical) vs precision (horizontal) chart illustrating: high accuracy/low precision, high accuracy/high precision, low accuracy/low precision, low accuracy/high precision
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 09/08/2026
Precision-Recall Plot: Clearly Explained 👇🏽
Side-by-side precision–recall plots for P:N = 1:1 and 1:3 illustrating a perfect classifier hugging the top and right, a dotted random baseline at precision equal to prevalence (0.5 vs 0.25), regions labeled GOOD/POOR, and that AUC close to 1 is better.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 08/08/2026
What's the difference between accuracy and precision?
Four bullseye targets on an accuracy (vertical) vs precision (horizontal) chart illustrating: high accuracy/low precision, high accuracy/high precision, low accuracy/low precision, low accuracy/high precision
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 07/08/2026
What is the Bias-Variance Tradeoff?  Many aspiring data scientists don't understand what bias and variance are and why it is a tradeoff. Here's an easy-to-understand explanation for you. 👇🏽
Diagram showing the bias–variance tradeoff with four bullseye targets: low bias/low variance hits clustered at center, high bias/low variance hits clustered off-center, low bias/high variance hits widely scattered around center, and high bias/high variance hits scattered and off-center; axes
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 06/08/2026
An Overview of Key Discrete Distributions 🧐
Educational poster titled "Discrete Distributions – Clearly explained" summarizing Bernoulli, Binomial, Poisson, Geometric and Uniform distributions with notation, PMFs, mean/variance, bar-chart visuals and short examples.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 02/08/2026
Understand SQL JOINS. Here's a good starting point.
Infographic titled "SQL Joins Visualization" showing example tables and Venn diagrams that illustrate inner, left, right, full and excluding joins, plus execution-order steps and sample SQL SELECT ... JOIN ... ON queries linking tables A and B.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 04/08/2026
Docker Commands and Main Components 🔥
Infographic summarizing core Docker concepts — Dockerfile, image, registry, container, volume, network, daemon, and engine — with brief explanations around a central Docker logo and a bottom section listing essential Docker CLI commands.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 06/08/2026
Your network is your greatest asset in the AI era. 🌐 Surround yourself with builders, thinkers, and innovators. Our community is designed to help you bridge the gap between “coding” and "engineering experts.” Meet your friends today: tinztwinshub.com/community/
Bold white word "Community" centered on a blue background, surrounded by faint social icons—speech bubbles, heart, thumbs-up, share node, play button and user silhouette—evoking online engagement and connection.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 05/08/2026
Cross Validation Overview 🔥
Infographic titled "Cross Validation" explaining model evaluation: definition, basic idea, goodness-of-fit and MSE formula, plus visual diagrams and comparisons of validation set approach, leave-one-out (LOOCV) and k‑fold cross-validation with advantages and disadvantages.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 04/08/2026
Docker Commands and Main Components 🔥
Infographic summarizing core Docker concepts — Dockerfile, image, registry, container, volume, network, daemon, and engine — with brief explanations around a central Docker logo and a bottom section listing essential Docker CLI commands.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 02/08/2026
Understand SQL JOINS. Here's a good starting point.
Infographic titled "SQL Joins Visualization" showing example tables and Venn diagrams that illustrate inner, left, right, full and excluding joins, plus execution-order steps and sample SQL SELECT ... JOIN ... ON queries linking tables A and B.
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 12/07/2026
🚀 Hidden Python Tips Stop wasting time on manual tasks with these hidden gems. - Convert images to LaTeX instantly - Automatically remove unused Python code - Speed up large Pandas operations FREE Guide: tinztwinshub.com/data-science...
tinztwinshub.com
7 Hidden Python Tips
Boost your Python skills with these 7 hidden tips for data scientists and engineers!
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 13/07/2026
Master Pandas DateTimeIndex for Time Series! 🚀 Unlock the full potential of financial time series analysis with our guide. - Master Pandas DateTimeIndex - Simplify complex time series processing - Expert tips for data scientists Read the full article here: tinztwinshub.com/data-science...
tinztwinshub.com
Demystifying DateTimeIndex in Pandas for Time Series Analysis
Unlock the secrets of time series analysis with our expert guide on Pandas DateTimeIndex!
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Reposted by Tinz Twins | AI and Coding
Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 13/07/2026
Curious how Artificial Neural Networks work in general?
Infographic explaining artificial neural networks: basic idea with a diagram of input, hidden and output layers showing nodes and weighted edges, arrows for feedforward and backpropagation, plus sections on feedforward, activation/weighting, backpropagation and training.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 14/07/2026
Master Stock Market Data with Resampling! 📈 Unlock deeper financial insights by mastering time-series manipulation in Python. - Use OpenBB for data loading - Master Pandas time resampling techniques - Analyze quarterly stock trends Read the full tutorial here: tinztwinshub.com/data-science...
tinztwinshub.com
Tesla’s Stock Market data - Mastering Time Resampling with OpenBB and Pandas
Master time resampling in financial analysis with OpenBB and Pandas to unlock insights from Tesla’s stock data.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 13/07/2026
Curious how Artificial Neural Networks work in general?
Infographic explaining artificial neural networks: basic idea with a diagram of input, hidden and output layers showing nodes and weighted edges, arrows for feedforward and backpropagation, plus sections on feedforward, activation/weighting, backpropagation and training.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 13/07/2026
Master Pandas DateTimeIndex for Time Series! 🚀 Unlock the full potential of financial time series analysis with our guide. - Master Pandas DateTimeIndex - Simplify complex time series processing - Expert tips for data scientists Read the full article here: tinztwinshub.com/data-science...
tinztwinshub.com
Demystifying DateTimeIndex in Pandas for Time Series Analysis
Unlock the secrets of time series analysis with our expert guide on Pandas DateTimeIndex!
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 12/07/2026
🚀 Hidden Python Tips Stop wasting time on manual tasks with these hidden gems. - Convert images to LaTeX instantly - Automatically remove unused Python code - Speed up large Pandas operations FREE Guide: tinztwinshub.com/data-science...
tinztwinshub.com
7 Hidden Python Tips
Boost your Python skills with these 7 hidden tips for data scientists and engineers!
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 11/07/2026
Convolutional Neural Networks Made Simple 🔥 CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
Diagram explaining convolutional neural networks: a 28×28 digit input flows through stacked convolutional and pooling layers (feature maps shown shrinking from 32×28×28 to 64×7×7) then flattening into a fully connected layer that outputs digit labels.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 11/07/2026
Master the Essentials of Explainable AI! 🚀 We break down the fundamental concepts of XAI for you. - Why transparency matters for ML - Essential XAI principles and goals - Achieving trust through explainability Our Deep-Dive: tinztwinshub.com/data-science...
tinztwinshub.com
Unlock the Blackbox - Demystifying Machine Learning Explainability!
Explore the essentials of Explainable AI and why transparency in ML is crucial for trust.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 10/07/2026
Master Secure File Transfers with Docker! 🚀 The easiest way to host your own secure file server is finally here. - Quick, containerized server deployment - Automated, secure file management - Perfect for DevOps engineers Learn how to set it up: tinztwinshub.com/software-eng...
tinztwinshub.com
Set up a Docker-based SFTP server. It’s open-source and secure!
Learn how to set up an SFTP server with Docker for secure data transfer and storage. It’s ideal for keeping your data safe!
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 10/07/2026
Variational Autoencoder (VAE) 🔥
Diagram illustrating a variational autoencoder: an encoder maps input x to latent parameters μ and σ, a latent sample z is drawn, and a decoder reconstructs x' from z, showing the probabilistic encoder/decoder and flow from input to latent representation to reconstruction.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 09/07/2026
Master Responsible LLM Application Development! 🚀 Learn how to build safe, efficient, and robust AI applications. - Prevent undesirable behaviors - Filter hateful speech automatically - Build safer, more reliable systems Read the full guide here: tinztwinshub.com/data-science...
tinztwinshub.com
Responsible Development of an LLM Application + Best Practices
Discover the best practices for developing LLM applications responsibly with our expert guide.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 09/07/2026
Autoencoder Made Simple 🔥
Diagram illustrating autoencoder concept: input x is compressed by encoder g(x) into latent representation z, then reconstructed by decoder f(z) to x'; includes bullet notes: compress input (encoding), reconstruct (decoding), semi‑supervised.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 08/07/2026
Can ML Predict Stock Market Trends? 🤔 Learn how to transform time-series data into a powerful classification problem using Python. - Master essential data preparation steps - Use OpenBB for financial data - Compare XGBoost, Random Forest, and more Full guide: tinztwinshub.com/data-science...
tinztwinshub.com
Convert a Time Series into a Classification Problem
Discover how to predict stock movements using AI with XGBoost, Random Forest, and Logistic Regression. All examples are in Python.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 08/07/2026
What is Tool Calling? 🧐 Tool calling refers to the ability of LLMs to interact with external tools, APIs, or systems to improve their functionality. Here’s how it works:
Diagram showing how a tool call flows between an Application, an API, and an LLM: a user prompt and tool definitions go to the API, the API sends system prompts and tool info to the LLM, function names and parameter values are exchanged, results return to the application, and a response is
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 07/07/2026
Mastering Search: From Keywords to AI! 🚀 Ever wondered how search engines actually find what you're looking for? - How keyword search works - Building search systems with Python - Enhancing search using language models Read the full deep dive here: tinztwinshub.com/software-eng...
tinztwinshub.com
Learn How Keyword Search Works and How Language Models can Improve it!
Discover how keyword search works and how language models can enhance search systems.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 07/07/2026
Agentic AI Design Patterns: Visually explained 🔥
Diagram titled Reflection Pattern showing a user sending a prompt to Agent A (an LLM) which returns a result, with dashed arrows indicating reflected text routed between Agent A and Agent B (both LLMs) to produce the output text.Tool Use Pattern diagram showing a user sending a prompt to an agent (LLM) that calls external tools—Python function, web search, knowledge database—and returns a result to the user.Planning Pattern diagram showing a user sending a prompt to a Planner Agent (contains an LLM) that returns a result, with dashed feedback arrows connecting the Planner to an Executor Agent for generating tasks and checking progress.Multi-agent LLM architecture diagram showing a user sending a prompt to Agent A (planner LLM) that delegates tasks via dashed arrows to specialized agents (Writer, Software Engineer, Code Executor / Agents B–D) which coordinate and return a combined result to the user.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 06/07/2026
Agentic Protocols 🔥 - MCP connects agents to tools. - A2A enables agents to communicate with other agents. - AG-UI connects agents to users.
Agent Protocol Stack showing a central Agent connecting to Tools (MCP), Users (AG-UI), and other Agents (A2A), with arrows illustrating protocol flows between tools, a single agent, users, and multiple agents.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 06/07/2026
Level Up Your Data Science Skills! 🚀 Understanding distribution assumptions is essential for any successful machine learning engineer. - Essential for ML algorithms - Visuals instead of just formulas - Bernoulli, Binomial, and more Boost your knowledge today: tinztwinshub.com/data-science...
tinztwinshub.com
Univariate Discrete Distributions - An Easy-to-Understand Explanation with Visuals
Explore univariate discrete distributions with simple math and visuals, perfect for aspiring data scientists — all examples in Python.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 05/07/2026
Retrieval Augmented Generation: Visually Explained 🧐
Infographic explaining Retrieval‑Augmented Generation (RAG), showing a five-step workflow and component diagram—knowledge base, chunking, embeddings model, vector database, and LLM—plus common use cases and pros and cons.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 05/07/2026
🚀 Deploy Docker Demo Apps in Minutes! Streamline your workflow by deploying directly from Docker Hub to Render for free. - No credit card required - Easy Docker Hub integration - Automated deployment workflow Our FREE Guide: tinztwinshub.com/software-eng...
tinztwinshub.com
How to Deploy a Web App With Docker on Render for Free?
Learn how to deploy a web app using Docker on Render for free, with no credit card required!
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 04/07/2026
If you want to become an AI Engineer in 2026, we would follow this roadmap. 10 steps. No fluff. Just pure learning that gets you ahead. 🔥
AI Engineering Roadmap
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 04/07/2026
A complete guide to using Pytest. - Simple pytest basics explained - Practical function testing examples - Understand pytest output clearly Full guide: tinztwinshub.com/software-eng...
tinztwinshub.com
Efficient Testing of Your Python Code With Pytest
Learn to use the package pytest for efficient Python code testing and ensure software reliability.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 03/07/2026
Master SEC Filings with EDGAR Tools! 🚀 Stop struggling with broken libraries—discover the ultimate Python package for seamless SEC data access. - Access all filings since 1994 easily - No API key required - Powerful filtering and querying capabilities Full guide: tinztwinshub.com/investment-r...
tinztwinshub.com
EDGAR Tools - An Awesome Python Package to Get SEC Filings
Discover the Python package EDGAR tools for accessing SEC filings.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 02/07/2026
Mastering KPIs in Data Science Projects 🚀 Learn how to bring transparency and control to complex data projects. - Avoid common data science project failures - Apply CRISP-DM for better control - Implement meaningful, project-specific KPIs Full guide: tinztwinshub.com/data-science...
tinztwinshub.com
Efficient Use of KPIs in Data Science Projects
Learn how to use KPIs effectively in data science projects to improve project management and ensure the successful implementation of machine learning projects.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 02/07/2026
Confusion Matrix: Less Confusing 🔥
Confusion matrix infographic explaining predicted vs true classes with labeled quadrants: True Negative (TN), False Positive (FP), False Negative (FN), True Positive (TP), plus a table of rates showing TPR, TNR, FNR, FPR formulas.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 02/07/2026
Stop Credit Card Fraud with AI! 💳 Learn how to build an advanced anomaly detector using Python and TensorFlow. - Detect fraudulent transactions - Master Autoencoder model implementation - Step-by-step guide Full guide: tinztwinshub.com/data-science...
tinztwinshub.com
Detection of Credit Card Fraud with an Autoencoder
Learn to detect credit card fraud using an autoencoder with Python and TensorFlow.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 01/07/2026
Deploy Your Own MLflow Workspace! 🚀 Stop losing track of your machine learning experiments and take full control of your workflow. - Set up MLflow with Docker - Manage models on-premise - Includes Postgres, SFTP, and JupyterLab Check out the full guide here: tinztwinshub.com/software-eng...
tinztwinshub.com
Deploy Your Own MLflow Workspace On-Premise with Docker
Set up an on-premise MLflow workspace with Docker for efficient model management, supporting multiple libraries and tracking.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 01/07/2026
ROC Plot Made Simple 🔥 You can use an ROC (Receiver Operating Characteristics) curve to evaluate the results of a classifier. The ROC curve represents the trade-off between the True positive rate (TPR) and the False positive rate (FPR).
ROC curve diagram showing a perfect classifier at the top-left (green line), a random classifier as the diagonal, labeled GOOD (upper-left) and POOR (lower-right), and note: "Area under the ROC curve = AUC score; A good classifier has an AUC-Score > 0.5."
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 30/06/2026
Master Interactive Charts with Plotly Express! Transform your data into stunning, interactive visual stories with ease. - Create beautiful Bubble charts easily - Visualize hierarchies with Sunburst plots - Build engaging Treemap visualizations quickly tinztwinshub.com/data-science...
tinztwinshub.com
Create Impressive Charts Using Plotly Express in Python
Learn to create interactive charts with Plotly in Python: Bubble, Sunburst, and Treemap visualizations made easy for engaging data storytelling.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 29/06/2026
Supercharge Your NumPy Code with NumExpr! 🚀 Stop waiting for large datasets to process and start optimizing your workflow today. - Speed up large array computations - Optimize CPU core utilization - Reduce memory overhead effectively Read the full guide here: tinztwinshub.com/data-science...
tinztwinshub.com
Speed Up your NumPy Code with NumExpr in Seconds
Discover how to supercharge your NumPy code for large datasets with NumExpr, achieving faster and more efficient data processing.
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 29/06/2026
Precision-Recall Plot: Clearly Explained 🔥
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Tinz Twins | AI and Coding @tinztwinshub.bsky.social · 28/06/2026
What's the difference between accuracy and precision?
Visualization: Precision and Accuracy
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