📌 The Machine Learning “Advent Calendar” Day 10: DBSCAN in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 5 min read
DBSCAN shows how far we can go with a very simple idea: count how many…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 5 min read
DBSCAN shows how far we can go with a very simple idea: count how many…
#DataScience #AI #Python
📌 How to Maximize Agentic Memory for Continual Learning
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-10 | ⏱️ Read time: 7 min read
Learn how to become an effective engineer with continual learning LLMs
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2025-12-10 | ⏱️ Read time: 7 min read
Learn how to become an effective engineer with continual learning LLMs
#DataScience #AI #Python
❤1👍1
📌 Don’t Build an ML Portfolio Without These Projects
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 8 min read
What recruiters are looking for in machine learning portfolios
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 8 min read
What recruiters are looking for in machine learning portfolios
#DataScience #AI #Python
❤1👍1
📌 Optimizing PyTorch Model Inference on AWS Graviton
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 11 min read
Tips for accelerating AI/ML on CPU — Part 2
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2025-12-10 | ⏱️ Read time: 11 min read
Tips for accelerating AI/ML on CPU — Part 2
#DataScience #AI #Python
🔍 Exploring the Power of Support Vector Machines (SVM) in Machine Learning!
🚀 Support Vector Machines are a powerful class of supervised learning algorithms that can be used for both classification and regression tasks. They have gained immense popularity due to their ability to handle complex datasets and deliver accurate predictions. Let's explore some key aspects that make SVMs stand out:
1️⃣ Robustness: SVMs are highly effective in handling high-dimensional data, making them suitable for various real-world applications such as text categorization and bioinformatics. Their robustness enables them to handle noise and outliers effectively.
2️⃣ Margin Maximization: One of the core principles behind SVM is maximizing the margin between different classes. By finding an optimal hyperplane that separates data points with the maximum margin, SVMs aim to achieve better generalization on unseen data.
3️⃣ Kernel Trick: The kernel trick is a game-changer when it comes to SVMs. It allows us to transform non-linearly separable data into higher-dimensional feature spaces where they become linearly separable. This technique opens up possibilities for solving complex problems that were previously considered challenging.
4️⃣ Regularization: SVMs employ regularization techniques like L1 or L2 regularization, which help prevent overfitting by penalizing large coefficients. This ensures better generalization performance on unseen data.
5️⃣ Versatility: SVMs offer various formulations such as C-SVM (soft-margin), ν-SVM (nu-Support Vector Machine), and ε-SVM (epsilon-Support Vector Machine). These formulations provide flexibility in handling different types of datasets and trade-offs between model complexity and error tolerance.
6️⃣ Interpretability: Unlike some black-box models, SVMs provide interpretability. The support vectors, which are the data points closest to the decision boundary, play a crucial role in defining the model. This interpretability helps in understanding the underlying patterns and decision-making process.
As machine learning continues to revolutionize industries, Support Vector Machines remain a valuable tool in our arsenal. Their ability to handle complex datasets, maximize margins, and transform non-linear data make them an essential technique for tackling challenging problems.
#MachineLearning #SupportVectorMachines #DataScience #ArtificialIntelligence #SVM
https://news.1rj.ru/str/DataScienceM✅ ✅
🚀 Support Vector Machines are a powerful class of supervised learning algorithms that can be used for both classification and regression tasks. They have gained immense popularity due to their ability to handle complex datasets and deliver accurate predictions. Let's explore some key aspects that make SVMs stand out:
1️⃣ Robustness: SVMs are highly effective in handling high-dimensional data, making them suitable for various real-world applications such as text categorization and bioinformatics. Their robustness enables them to handle noise and outliers effectively.
2️⃣ Margin Maximization: One of the core principles behind SVM is maximizing the margin between different classes. By finding an optimal hyperplane that separates data points with the maximum margin, SVMs aim to achieve better generalization on unseen data.
3️⃣ Kernel Trick: The kernel trick is a game-changer when it comes to SVMs. It allows us to transform non-linearly separable data into higher-dimensional feature spaces where they become linearly separable. This technique opens up possibilities for solving complex problems that were previously considered challenging.
4️⃣ Regularization: SVMs employ regularization techniques like L1 or L2 regularization, which help prevent overfitting by penalizing large coefficients. This ensures better generalization performance on unseen data.
5️⃣ Versatility: SVMs offer various formulations such as C-SVM (soft-margin), ν-SVM (nu-Support Vector Machine), and ε-SVM (epsilon-Support Vector Machine). These formulations provide flexibility in handling different types of datasets and trade-offs between model complexity and error tolerance.
6️⃣ Interpretability: Unlike some black-box models, SVMs provide interpretability. The support vectors, which are the data points closest to the decision boundary, play a crucial role in defining the model. This interpretability helps in understanding the underlying patterns and decision-making process.
As machine learning continues to revolutionize industries, Support Vector Machines remain a valuable tool in our arsenal. Their ability to handle complex datasets, maximize margins, and transform non-linear data make them an essential technique for tackling challenging problems.
#MachineLearning #SupportVectorMachines #DataScience #ArtificialIntelligence #SVM
https://news.1rj.ru/str/DataScienceM
Please open Telegram to view this post
VIEW IN TELEGRAM
❤4
📌 The Machine Learning “Advent Calendar” Day 9: LOF in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-09 | ⏱️ Read time: 7 min read
In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances,…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-09 | ⏱️ Read time: 7 min read
In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances,…
#DataScience #AI #Python
❤2
📌 The Machine Learning “Advent Calendar” Day 11: Linear Regression in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-11 | ⏱️ Read time: 12 min read
Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-11 | ⏱️ Read time: 12 min read
Linear Regression looks simple, but it introduces the core ideas of modern machine learning: loss…
#DataScience #AI #Python
❤1
🤖🧠 How to Run and Fine-Tune Kimi K2 Thinking Locally with Unsloth
🗓️ 11 Dec 2025
📚 AI News & Trends
The demand for efficient and powerful large language models (LLMs) continues to rise as developers and researchers seek new ways to optimize reasoning, coding, and conversational AI performance. One of the most impressive open-source AI systems available today is Kimi K2 Thinking, created by Moonshot AI. Through collaboration with Unsloth, users can now fine-tune and ...
#KimiK2Thinking #Unsloth #LLMs #LargeLanguageModels #AI #FineTuning
🗓️ 11 Dec 2025
📚 AI News & Trends
The demand for efficient and powerful large language models (LLMs) continues to rise as developers and researchers seek new ways to optimize reasoning, coding, and conversational AI performance. One of the most impressive open-source AI systems available today is Kimi K2 Thinking, created by Moonshot AI. Through collaboration with Unsloth, users can now fine-tune and ...
#KimiK2Thinking #Unsloth #LLMs #LargeLanguageModels #AI #FineTuning
❤1
📌 Drawing Shapes with the Python Turtle Module
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
A step-by-step tutorial that explores the Python Turtle Module
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
A step-by-step tutorial that explores the Python Turtle Module
#DataScience #AI #Python
❤1
📌 7 Pandas Performance Tricks Every Data Scientist Should Know
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
What I’ve learned about making Pandas faster after too many slow notebooks and frozen sessions
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
What I’ve learned about making Pandas faster after too many slow notebooks and frozen sessions
#DataScience #AI #Python
❤1💩1
This media is not supported in your browser
VIEW IN TELEGRAM
K-means is one of the most widely used clustering algorithms in data science and machine learning. A key part of the algorithm is convergence (a process in which cluster centers and point assignments gradually stabilize due to repeated updates). To put it simply, understanding how and why convergence occurs helps to obtain reliable and meaningful clustering results.
✔️ It converges quickly on most datasets, making it effective for large-scale tasks
✔️ It offers a simple and interpretable structure for identifying groups
✔️ It scales well on large data sets due to its low computational complexity
❌ The results heavily depend on the initial clustering initialization
❌ It can distort the data structure if the features are incorrectly scaled
❌ It can generate empty or unstable clusters if configured incorrectly
To ensure stable convergence:
- Use k-means++ for a more informed selection of initial centers
- Apply feature scaling to prevent variables with large scales from dominating
- Set appropriate values for the iteration limit and convergence threshold
The image shows the K-means convergence process. Data points are assigned to the nearest center based on the square of the distance. After that, each center is recalculated as the average of all points assigned to it. These steps are repeated until the positions of the centers no longer change significantly.
👉 @DataScienceM
To ensure stable convergence:
- Use k-means++ for a more informed selection of initial centers
- Apply feature scaling to prevent variables with large scales from dominating
- Set appropriate values for the iteration limit and convergence threshold
The image shows the K-means convergence process. Data points are assigned to the nearest center based on the square of the distance. After that, each center is recalculated as the average of all points assigned to it. These steps are repeated until the positions of the centers no longer change significantly.
Please open Telegram to view this post
VIEW IN TELEGRAM
❤3👍1
📌 How Agent Handoffs Work in Multi-Agent Systems
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2025-12-11 | ⏱️ Read time: 9 min read
Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph
#DataScience #AI #Python
❤2
📌 The Machine Learning “Advent Calendar” Day 12: Logistic Regression in Excel
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-12 | ⏱️ Read time: 7 min read
In this article, we rebuild Logistic Regression step by step directly in Excel. Starting from…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2025-12-12 | ⏱️ Read time: 7 min read
In this article, we rebuild Logistic Regression step by step directly in Excel. Starting from…
#DataScience #AI #Python
❤1
📌 Decentralized Computation: The Hidden Principle Behind Deep Learning
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-12 | ⏱️ Read time: 12 min read
Most breakthroughs in deep learning — from simple neural networks to large language models —…
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-12-12 | ⏱️ Read time: 12 min read
Most breakthroughs in deep learning — from simple neural networks to large language models —…
#DataScience #AI #Python
📌 EDA in Public (Part 1): Cleaning and Exploring Sales Data with Pandas
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-12 | ⏱️ Read time: 11 min read
Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA…
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2025-12-12 | ⏱️ Read time: 11 min read
Hey everyone! Welcome to the start of a major data journey that I’m calling “EDA…
#DataScience #AI #Python
❤1
🤖🧠 S3PRL Toolkit: Advancing Self-Supervised Speech Representation Learning
🗓️ 13 Dec 2025
📚 AI News & Trends
The field of speech technology has witnessed a transformative shift in recent years, powered by the rise of self-supervised learning (SSL). Instead of relying on large amounts of labeled data, self-supervised models learn from the patterns and structures inherent in raw audio, enabling powerful and general-purpose speech representations. At the forefront of this innovation stands ...
#S3PRL #SelfSupervisedLearning #SpeechTechnology #SSL #SpeechRepresentationLearning #AI
🗓️ 13 Dec 2025
📚 AI News & Trends
The field of speech technology has witnessed a transformative shift in recent years, powered by the rise of self-supervised learning (SSL). Instead of relying on large amounts of labeled data, self-supervised models learn from the patterns and structures inherent in raw audio, enabling powerful and general-purpose speech representations. At the forefront of this innovation stands ...
#S3PRL #SelfSupervisedLearning #SpeechTechnology #SSL #SpeechRepresentationLearning #AI
❤1
📌 Spectral Community Detection in Clinical Knowledge Graphs
🗂 Category: GRAPH THEORY
🕒 Date: 2025-12-12 | ⏱️ Read time: 22 min read
Introduction How do we identify latent groups of patients in a large cohort? How can…
#DataScience #AI #Python
🗂 Category: GRAPH THEORY
🕒 Date: 2025-12-12 | ⏱️ Read time: 22 min read
Introduction How do we identify latent groups of patients in a large cohort? How can…
#DataScience #AI #Python
❤1
💡 Cons & Pros of Naive Bayes Algorithm
Naive Bayes is a #classification algorithm that is widely used in #machinelearning and #naturallanguageprocessing tasks. It is based on Bayes’ theorem, which describes the probability of an event based on prior knowledge of conditions related to that event. While Naive Bayes has its advantages, it also has some limitations.
💡 Pros of Naive Bayes:
1️⃣ Simplicity and efficiency
Naive Bayes is a simple and computationally efficient algorithm that is easy to understand and implement. It requires a relatively small amount of training data to estimate the parameters needed for classification.
2️⃣ Fast training and prediction
Due to its simplicity, Naive Bayes has fast training and inference compared to more complex algorithms, which makes it suitable for large-scale and real-time applications.
3️⃣ Handles high-dimensional data
Naive Bayes performs well even when the number of features is large compared to the number of samples. It scales effectively in high-dimensional spaces, which is why it is popular in text classification and spam filtering.
4️⃣ Works well with categorical data
Naive Bayes naturally supports categorical or discrete features, and variants like Multinomial and Bernoulli Naive Bayes are especially effective for text and count data. Continuous features can be handled with Gaussian Naive Bayes or by discretization.
5️⃣ Robust to many irrelevant features
Because each feature contributes independently to the final probability, many irrelevant features tend not to hurt performance severely, especially when there is enough data.
💡 Cons of Naive Bayes:
1️⃣ Strong independence assumption
The core limitation is the assumption that features are conditionally independent given the class, which is rarely true in real-world data and can degrade performance when strong feature interactions exist.
2️⃣ Lack of feature interactions
Naive Bayes cannot model complex relationships or interactions between features. Each feature influences the prediction on its own, which limits the model’s expressiveness compared to methods like trees, SVMs, or neural networks.
3️⃣ Sensitivity to imbalanced data
With highly imbalanced class distributions, posterior probabilities can become dominated by the majority class, causing poor performance on minority classes unless you rebalance or adjust priors.
4️⃣ Limited representation power
Naive Bayes works best when class boundaries are relatively simple. For complex, non-linear decision boundaries, more flexible models (e.g., SVMs, ensembles, neural networks) usually achieve higher accuracy.
5️⃣ Reliance on good-quality data
The algorithm is sensitive to noisy data, missing values, and rare events. Zero-frequency problems (unseen feature–class combinations) can cause zero probabilities unless techniques like Laplace smoothing are used.
Naive Bayes is a #classification algorithm that is widely used in #machinelearning and #naturallanguageprocessing tasks. It is based on Bayes’ theorem, which describes the probability of an event based on prior knowledge of conditions related to that event. While Naive Bayes has its advantages, it also has some limitations.
💡 Pros of Naive Bayes:
1️⃣ Simplicity and efficiency
Naive Bayes is a simple and computationally efficient algorithm that is easy to understand and implement. It requires a relatively small amount of training data to estimate the parameters needed for classification.
2️⃣ Fast training and prediction
Due to its simplicity, Naive Bayes has fast training and inference compared to more complex algorithms, which makes it suitable for large-scale and real-time applications.
3️⃣ Handles high-dimensional data
Naive Bayes performs well even when the number of features is large compared to the number of samples. It scales effectively in high-dimensional spaces, which is why it is popular in text classification and spam filtering.
4️⃣ Works well with categorical data
Naive Bayes naturally supports categorical or discrete features, and variants like Multinomial and Bernoulli Naive Bayes are especially effective for text and count data. Continuous features can be handled with Gaussian Naive Bayes or by discretization.
5️⃣ Robust to many irrelevant features
Because each feature contributes independently to the final probability, many irrelevant features tend not to hurt performance severely, especially when there is enough data.
💡 Cons of Naive Bayes:
1️⃣ Strong independence assumption
The core limitation is the assumption that features are conditionally independent given the class, which is rarely true in real-world data and can degrade performance when strong feature interactions exist.
2️⃣ Lack of feature interactions
Naive Bayes cannot model complex relationships or interactions between features. Each feature influences the prediction on its own, which limits the model’s expressiveness compared to methods like trees, SVMs, or neural networks.
3️⃣ Sensitivity to imbalanced data
With highly imbalanced class distributions, posterior probabilities can become dominated by the majority class, causing poor performance on minority classes unless you rebalance or adjust priors.
4️⃣ Limited representation power
Naive Bayes works best when class boundaries are relatively simple. For complex, non-linear decision boundaries, more flexible models (e.g., SVMs, ensembles, neural networks) usually achieve higher accuracy.
5️⃣ Reliance on good-quality data
The algorithm is sensitive to noisy data, missing values, and rare events. Zero-frequency problems (unseen feature–class combinations) can cause zero probabilities unless techniques like Laplace smoothing are used.
❤2
How could telegram be used for marketing?
Help me with my bachelor thesis 🙏
Quick survey (max 3 minutes).
3 people win $10 USDT 🎁
Thanks!
https://qualtricsxmtfg9cjqzs.qualtrics.com/jfe/form/SV_eUKFbnB166Qz5FI
Sponsored By WaybienAds
Help me with my bachelor thesis 🙏
Quick survey (max 3 minutes).
3 people win $10 USDT 🎁
Thanks!
https://qualtricsxmtfg9cjqzs.qualtrics.com/jfe/form/SV_eUKFbnB166Qz5FI
Sponsored By WaybienAds