Tinz Twins | AI and Coding
@tinztwinshub.bsky.social
Data Scientists | We write about AI, Software Engineering and Investment Research. | Founder towardsfinance.com
👉🏽 FREE ML cheat sheets: tinztwinshub.com/blog
👉🏽 FREE ML cheat sheets: tinztwinshub.com/blog
Ensemble methods (Boosting, Bagging, and Stacking) visually explained! 👇🏽
November 22, 2025 at 7:08 AM
Ensemble methods (Boosting, Bagging, and Stacking) visually explained! 👇🏽
Machine Learning: A Visual Guide to Boosting
Boosting is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Our FREE cheat sheets: buff.ly/69Jw60g
Boosting is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Our FREE cheat sheets: buff.ly/69Jw60g
November 21, 2025 at 8:02 PM
Machine Learning: A Visual Guide to Boosting
Boosting is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Our FREE cheat sheets: buff.ly/69Jw60g
Boosting is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Our FREE cheat sheets: buff.ly/69Jw60g
Machine Learning: A Visual Guide to Bagging 🔥
Bagging (short for Bootstrapped Aggregation) is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Bagging (short for Bootstrapped Aggregation) is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
November 20, 2025 at 6:51 PM
Machine Learning: A Visual Guide to Bagging 🔥
Bagging (short for Bootstrapped Aggregation) is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Bagging (short for Bootstrapped Aggregation) is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
Machine Learning: A Visual Guide to Stacking 🔥
Stacking is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
FREE cheat sheets: buff.ly/69Jw60g
Stacking is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
FREE cheat sheets: buff.ly/69Jw60g
November 19, 2025 at 7:54 PM
Machine Learning: A Visual Guide to Stacking 🔥
Stacking is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
FREE cheat sheets: buff.ly/69Jw60g
Stacking is an ensemble method. An ensemble combines multiple models to create an even more powerful model.
💡 Primary idea: The aggregation of multiple models helps to reduce the weaknesses of individual models.
FREE cheat sheets: buff.ly/69Jw60g
Linear Regression is a simple statistical regression method, making it perfect for beginners in predictive analysis.
In simple Linear Regression, we use one independent variable to predict a dependent variable.
🎯 Goal: Find a line "Best fit" that represents the trend in the data.
In simple Linear Regression, we use one independent variable to predict a dependent variable.
🎯 Goal: Find a line "Best fit" that represents the trend in the data.
November 17, 2025 at 5:43 PM
Linear Regression is a simple statistical regression method, making it perfect for beginners in predictive analysis.
In simple Linear Regression, we use one independent variable to predict a dependent variable.
🎯 Goal: Find a line "Best fit" that represents the trend in the data.
In simple Linear Regression, we use one independent variable to predict a dependent variable.
🎯 Goal: Find a line "Best fit" that represents the trend in the data.
Support Vector Machine (SVM): Clearly explained
Imagine you have a set of points on a piece of paper, and you want to draw a line that separates them into two groups. That's what SVMs do.
🎯 Support Vector Machine (SVM) is like finding the best line that creates the widest gap between these groups.
Imagine you have a set of points on a piece of paper, and you want to draw a line that separates them into two groups. That's what SVMs do.
🎯 Support Vector Machine (SVM) is like finding the best line that creates the widest gap between these groups.
November 16, 2025 at 2:22 PM
Support Vector Machine (SVM): Clearly explained
Imagine you have a set of points on a piece of paper, and you want to draw a line that separates them into two groups. That's what SVMs do.
🎯 Support Vector Machine (SVM) is like finding the best line that creates the widest gap between these groups.
Imagine you have a set of points on a piece of paper, and you want to draw a line that separates them into two groups. That's what SVMs do.
🎯 Support Vector Machine (SVM) is like finding the best line that creates the widest gap between these groups.
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November 16, 2025 at 8:44 AM
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🤔 Want to understand how a simple artificial neural network learns?
Let's explore the math behind it👇🏽
Let's explore the math behind it👇🏽
November 15, 2025 at 3:57 PM
🤔 Want to understand how a simple artificial neural network learns?
Let's explore the math behind it👇🏽
Let's explore the math behind it👇🏽
What is a perceptron, and how does it work? 🧐
November 15, 2025 at 7:08 AM
What is a perceptron, and how does it work? 🧐
All you need to know about a basic neural network! 🧐
🔄 Repost to help others find this post!
🔄 Repost to help others find this post!
November 14, 2025 at 8:01 PM
All you need to know about a basic neural network! 🧐
🔄 Repost to help others find this post!
🔄 Repost to help others find this post!
Convolutional Neural Networks: Clearly explained! 🔥
CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
🔄 Repost to help others find this post!
CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
🔄 Repost to help others find this post!
November 13, 2025 at 6:51 PM
Convolutional Neural Networks: Clearly explained! 🔥
CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
🔄 Repost to help others find this post!
CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
🔄 Repost to help others find this post!
Variational Autoencoder (VAE) 🔥
VAEs combine Bayesian graph models and deep neural networks. You can use VAEs in anomaly detection or content generation.
🔄 Repost to help others find this post!
VAEs combine Bayesian graph models and deep neural networks. You can use VAEs in anomaly detection or content generation.
🔄 Repost to help others find this post!
November 11, 2025 at 6:37 PM
Variational Autoencoder (VAE) 🔥
VAEs combine Bayesian graph models and deep neural networks. You can use VAEs in anomaly detection or content generation.
🔄 Repost to help others find this post!
VAEs combine Bayesian graph models and deep neural networks. You can use VAEs in anomaly detection or content generation.
🔄 Repost to help others find this post!
How does an autoencoder work? 🧐
Autoencoders are artificial neural networks. They are very often used in anomaly detection, dimension reduction, ...
🔄 Repost to help others find this post!
Autoencoders are artificial neural networks. They are very often used in anomaly detection, dimension reduction, ...
🔄 Repost to help others find this post!
November 10, 2025 at 5:43 PM
How does an autoencoder work? 🧐
Autoencoders are artificial neural networks. They are very often used in anomaly detection, dimension reduction, ...
🔄 Repost to help others find this post!
Autoencoders are artificial neural networks. They are very often used in anomaly detection, dimension reduction, ...
🔄 Repost to help others find this post!
Prompt Engineering for Developers: Cheat Sheet 🔥
November 7, 2025 at 8:02 PM
Prompt Engineering for Developers: Cheat Sheet 🔥
What is Tool Calling? 🧐
November 6, 2025 at 6:51 PM
What is Tool Calling? 🧐
Agentic AI Design Patterns: Visually explained 🔥
November 5, 2025 at 7:54 PM
Agentic AI Design Patterns: Visually explained 🔥
The Agent Protocol Stack 🔥
- MCP connects agents to tools.
- A2A enables agents to communicate with other agents.
- AG-UI connects agents to users.
- MCP connects agents to tools.
- A2A enables agents to communicate with other agents.
- AG-UI connects agents to users.
November 4, 2025 at 6:37 PM
The Agent Protocol Stack 🔥
- MCP connects agents to tools.
- A2A enables agents to communicate with other agents.
- AG-UI connects agents to users.
- MCP connects agents to tools.
- A2A enables agents to communicate with other agents.
- AG-UI connects agents to users.
Retrieval Augmented Generation (RAG) Visually Explained 🧐
November 3, 2025 at 5:43 PM
Retrieval Augmented Generation (RAG) Visually Explained 🧐
Confusion Matrix - Less confusing
🔄 Repost to help others find this post!
🔄 Repost to help others find this post!
November 1, 2025 at 7:08 AM
Confusion Matrix - Less confusing
🔄 Repost to help others find this post!
🔄 Repost to help others find this post!
ROC plot - Clearly explained 👇🏽
💡 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).
#DataScience #MachineLearning
💡 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).
#DataScience #MachineLearning
October 31, 2025 at 8:02 PM
ROC plot - Clearly explained 👇🏽
💡 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).
#DataScience #MachineLearning
💡 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).
#DataScience #MachineLearning
Precision-Recall plot - Clearly explained 👇🏽
October 30, 2025 at 6:51 PM
Precision-Recall plot - Clearly explained 👇🏽
What's the difference between accuracy and precision?
October 29, 2025 at 7:54 PM
What's the difference between accuracy and precision?
🤔 What is the Bias-Variance Tradeoff?
The bias-variance tradeoff exists because bias and variance are inversely correlated. Reducing the bias means that the model becomes more complex. On the other hand, this increases the variance of the model.
The bias-variance tradeoff exists because bias and variance are inversely correlated. Reducing the bias means that the model becomes more complex. On the other hand, this increases the variance of the model.
October 28, 2025 at 6:37 PM
🤔 What is the Bias-Variance Tradeoff?
The bias-variance tradeoff exists because bias and variance are inversely correlated. Reducing the bias means that the model becomes more complex. On the other hand, this increases the variance of the model.
The bias-variance tradeoff exists because bias and variance are inversely correlated. Reducing the bias means that the model becomes more complex. On the other hand, this increases the variance of the model.
An Overview of Key Discrete Distributions 🧐
October 27, 2025 at 5:43 PM
An Overview of Key Discrete Distributions 🧐
Understanding the Bernoulli Distribution 🔥
💡 A Bernoulli distribution with parameter p exists if a random variable X has two possible outcomes (0 or 1). X=1 (success) occurs with a probability p, and X=0 (failure) occurs with a probability 1-p.
💡 A Bernoulli distribution with parameter p exists if a random variable X has two possible outcomes (0 or 1). X=1 (success) occurs with a probability p, and X=0 (failure) occurs with a probability 1-p.
October 26, 2025 at 8:44 AM
Understanding the Bernoulli Distribution 🔥
💡 A Bernoulli distribution with parameter p exists if a random variable X has two possible outcomes (0 or 1). X=1 (success) occurs with a probability p, and X=0 (failure) occurs with a probability 1-p.
💡 A Bernoulli distribution with parameter p exists if a random variable X has two possible outcomes (0 or 1). X=1 (success) occurs with a probability p, and X=0 (failure) occurs with a probability 1-p.