📌 How To Produce Ultra-Compact Vector Graphic Plots With Orthogonal Distance Fitting
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 11 min read
Generate high-quality, minimal SVG plots by fitting Bézier curves with an ODF algorithm.
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 11 min read
Generate high-quality, minimal SVG plots by fitting Bézier curves with an ODF algorithm.
#DataScience #AI #Python
❤1
📌 Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both.
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 16 min read
Inside disaggregated LLM inference — the architecture shift behind 2-4x cost reduction that most ML…
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 16 min read
Inside disaggregated LLM inference — the architecture shift behind 2-4x cost reduction that most ML…
#DataScience #AI #Python
🔍 Exploring the Power of Minkowski Distance in Data Analysis 📊
Minkowski distance is a mathematical measure used to calculate the distance between two points in a multi-dimensional space. It's an extension of the more commonly known Euclidean distance, which we often encounter in our daily lives. However, Minkowski distance offers additional flexibility by allowing us to adjust its behavior based on a parameter called "p."
The formula for Minkowski distance is as follows:
D(x, y) = (∑|xi - yi|^p)^(1/p)
Here, xi and yi represent the coordinates of two points in the dataset. By varying the value of "p," we can adapt the calculation to suit different scenarios:
1️⃣ When p = 1, it becomes Manhattan distance (also known as City Block or Taxicab distance). It measures the sum of absolute differences between corresponding coordinates. This metric is useful when movement can only occur along straight lines.
2️⃣ When p = 2, it reduces to Euclidean distance. It calculates the straight-line distance between two points and is widely used across various fields.
3️⃣ When p → ∞, it represents Chebyshev distance. This measure considers only the maximum difference between coordinates and is particularly useful when movement can occur diagonally.
By leveraging Minkowski distance with different values of "p," we gain flexibility in analyzing data based on specific requirements and characteristics of our dataset.
Applications of Minkowski distance are vast and diverse:
✅ Clustering Analysis: It helps identify similar groups or clusters within datasets by measuring distances between points.
✅ Recommender Systems: By calculating distances between users or items based on their attributes, Minkowski distance can assist in generating personalized recommendations.
✅ Anomaly Detection: It aids in identifying outliers or anomalies by measuring the deviation of a data point from the rest.
✅ Image Processing: Minkowski distance plays a crucial role in image comparison, object recognition, and pattern matching tasks.
Understanding Minkowski distance opens up exciting possibilities for data scientists, analysts, and researchers to gain deeper insights into their datasets and make informed decisions. 📈
So, next time you encounter multi-dimensional data analysis challenges, remember to explore the power of Minkowski distance!🚀
https://t.iss.one/DataScienceM✈️
Minkowski distance is a mathematical measure used to calculate the distance between two points in a multi-dimensional space. It's an extension of the more commonly known Euclidean distance, which we often encounter in our daily lives. However, Minkowski distance offers additional flexibility by allowing us to adjust its behavior based on a parameter called "p."
The formula for Minkowski distance is as follows:
D(x, y) = (∑|xi - yi|^p)^(1/p)
Here, xi and yi represent the coordinates of two points in the dataset. By varying the value of "p," we can adapt the calculation to suit different scenarios:
1️⃣ When p = 1, it becomes Manhattan distance (also known as City Block or Taxicab distance). It measures the sum of absolute differences between corresponding coordinates. This metric is useful when movement can only occur along straight lines.
2️⃣ When p = 2, it reduces to Euclidean distance. It calculates the straight-line distance between two points and is widely used across various fields.
3️⃣ When p → ∞, it represents Chebyshev distance. This measure considers only the maximum difference between coordinates and is particularly useful when movement can occur diagonally.
By leveraging Minkowski distance with different values of "p," we gain flexibility in analyzing data based on specific requirements and characteristics of our dataset.
Applications of Minkowski distance are vast and diverse:
✅ Clustering Analysis: It helps identify similar groups or clusters within datasets by measuring distances between points.
✅ Recommender Systems: By calculating distances between users or items based on their attributes, Minkowski distance can assist in generating personalized recommendations.
✅ Anomaly Detection: It aids in identifying outliers or anomalies by measuring the deviation of a data point from the rest.
✅ Image Processing: Minkowski distance plays a crucial role in image comparison, object recognition, and pattern matching tasks.
Understanding Minkowski distance opens up exciting possibilities for data scientists, analysts, and researchers to gain deeper insights into their datasets and make informed decisions. 📈
So, next time you encounter multi-dimensional data analysis challenges, remember to explore the power of Minkowski distance!
https://t.iss.one/DataScienceM
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📌 5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: Upcoming Webinar
🗂 Category: TDS WEBINARS
🕒 Date: 2026-04-15 | ⏱️ Read time: 5 min read
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical…
#DataScience #AI #Python
🗂 Category: TDS WEBINARS
🕒 Date: 2026-04-15 | ⏱️ Read time: 5 min read
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical…
#DataScience #AI #Python
📌 From Pixels to DNA: Why the Future of Compression Is About Every Kind of Data
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-15 | ⏱️ Read time: 21 min read
It’s not about audio and video anymore
#DataScience #AI #Python
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-15 | ⏱️ Read time: 21 min read
It’s not about audio and video anymore
#DataScience #AI #Python
📌 From OpenStreetMap to Power BI: Visualizing Wild Swimming Locations
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-15 | ⏱️ Read time: 19 min read
How to turn OpenStreetMap data into an interactive map of wild swimming spots using Overpass…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-15 | ⏱️ Read time: 19 min read
How to turn OpenStreetMap data into an interactive map of wild swimming spots using Overpass…
#DataScience #AI #Python
📌 RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-14 | ⏱️ Read time: 14 min read
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows.…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-14 | ⏱️ Read time: 14 min read
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows.…
#DataScience #AI #Python
📌 Your Chunks Failed Your RAG in Production
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-16 | ⏱️ Read time: 22 min read
The upstream decision no model, or LLM can fix once you get it wrong
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-16 | ⏱️ Read time: 22 min read
The upstream decision no model, or LLM can fix once you get it wrong
#DataScience #AI #Python
❤1
🚀 Why Modern AI Runs on GPUs and TPUs Instead of CPUs 🤖
AI models are essentially large matrix multiplication engines 🧮.
Training and inference involve billions or even trillions of tensor operations like:
👉 [Input Tensor] × [Weight Matrix] = Output ⚡️
The speed of these computations depends heavily on the hardware architecture 🏗.
Traditional CPUs execute operations sequentially ⏳. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads 🐢.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency 🐌.
👉 GPUs solve this with parallelism 🚀
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel 🔄.
Example:
Training a CNN for image classification:
- CPU training time → several hours ⏰
- GPU training time → minutes ⚡️
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads 🔧.
👉 TPUs go even further 🛸
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication 📐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements 🌊.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines 🚄.
Typical latency differences ⏱️
CPU → Seconds
GPU → Milliseconds
TPU → Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck 🚧.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently 🏢.
💡Key takeaway
AI progress is not only about better algorithms 🧠. It is also about better compute architecture 🔌.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
AI models are essentially large matrix multiplication engines 🧮.
Training and inference involve billions or even trillions of tensor operations like:
👉 [Input Tensor] × [Weight Matrix] = Output ⚡️
The speed of these computations depends heavily on the hardware architecture 🏗.
Traditional CPUs execute operations sequentially ⏳. A few powerful cores handle tasks one after another. This design is excellent for general purpose computing but inefficient for massive tensor workloads 🐢.
Example:
A transformer model performing attention calculations may require billions of multiplications. A CPU processes them sequentially which increases latency 🐌.
👉 GPUs solve this with parallelism 🚀
GPUs contain thousands of smaller cores designed to execute many matrix operations simultaneously. Instead of one operation at a time, thousands run in parallel 🔄.
Example:
Training a CNN for image classification:
- CPU training time → several hours ⏰
- GPU training time → minutes ⚡️
Frameworks like PyTorch and TensorFlow leverage CUDA cores to parallelize tensor computations across thousands of threads 🔧.
👉 TPUs go even further 🛸
TPUs are purpose built accelerators for deep learning workloads. They use systolic array architecture optimized for dense matrix multiplication 📐.
Instead of sending data back and forth between memory and compute units, data flows directly through a grid of processing elements 🌊.
Example:
Large language models like BERT or PaLM run inference much faster on TPUs due to optimized tensor pipelines 🚄.
Typical latency differences ⏱️
CPU → Seconds
GPU → Milliseconds
TPU → Microseconds
As models scale to billions of parameters, hardware architecture becomes the real bottleneck 🚧.
That is why modern AI infrastructure relies on GPU clusters and TPU pods to train and serve large models efficiently 🏢.
💡Key takeaway
AI progress is not only about better algorithms 🧠. It is also about better compute architecture 🔌.
#AI #MachineLearning #DeepLearning #GPUs #TPUs #LLM #DataScience
#ArtificialIntelligence
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📌 Building My Own Personal AI Assistant: A Chronicle, Part 2
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-16 | ⏱️ Read time: 9 min read
Building a personal AI assistant is rarely a single, monolithic effort. In this piece, I…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-16 | ⏱️ Read time: 9 min read
Building a personal AI assistant is rarely a single, monolithic effort. In this piece, I…
#DataScience #AI #Python
📌 memweave: Zero-Infra AI Agent Memory with Markdown and SQLite — No Vector Database Required
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-16 | ⏱️ Read time: 17 min read
The problem with agent memory today
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-16 | ⏱️ Read time: 17 min read
The problem with agent memory today
#DataScience #AI #Python
❤1
📌 Introduction to Deep Evidential Regression for Uncertainty Quantification
🗂 Category: DEEP LEARNING
🕒 Date: 2026-04-16 | ⏱️ Read time: 12 min read
Machine learning models can be confident even when they shouldn’t be. This article introduces Deep…
#DataScience #AI #Python
🗂 Category: DEEP LEARNING
🕒 Date: 2026-04-16 | ⏱️ Read time: 12 min read
Machine learning models can be confident even when they shouldn’t be. This article introduces Deep…
#DataScience #AI #Python
Forwarded from Machine Learning with Python
🚀 Thrilled to announce a major milestone in our collective upskilling journey! 🌟
I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
I am incredibly excited to share a curated ecosystem of high-impact resources focused on Machine Learning and Artificial Intelligence. By consolidating a comprehensive library of PDFs—from foundational onboarding to advanced strategic insights—into a single, unified repository, we are effectively eliminating search friction and accelerating our learning velocity. 📚✨
This initiative represents a powerful opportunity to align our technical growth with future-ready priorities, ensuring we are always ahead of the curve. 💡🔗
⛓️ Unlock your potential here:
https://github.com/Ramakm/AI-ML-Book-References
#MachineLearning #AI #ContinuousLearning #GrowthMindset #TechCommunity #OpenSource
❤5
📌 How to Maximize Claude Cowork
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 9 min read
Learn how to get the most out of Claude Cowork
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 9 min read
Learn how to get the most out of Claude Cowork
#DataScience #AI #Python
❤1
📌 Beyond Prompting: Using Agent Skills in Data Science
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-17 | ⏱️ Read time: 7 min read
How I turned my eight-year weekly visualization habit into a reusable AI workflow
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-17 | ⏱️ Read time: 7 min read
How I turned my eight-year weekly visualization habit into a reusable AI workflow
#DataScience #AI #Python
❤1
📌 You Don’t Need Many Labels to Learn
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-17 | ⏱️ Read time: 10 min read
What if an unsupervised model could become a strong classifier with only a handful of…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-17 | ⏱️ Read time: 10 min read
What if an unsupervised model could become a strong classifier with only a handful of…
#DataScience #AI #Python
📌 6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-17 | ⏱️ Read time: 11 min read
From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations…
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-17 | ⏱️ Read time: 11 min read
From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations…
#DataScience #AI #Python
📌 A Practical Guide to Memory for Autonomous LLM Agents
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-17 | ⏱️ Read time: 14 min read
Architectures, pitfalls, and patterns that work
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-17 | ⏱️ Read time: 14 min read
Architectures, pitfalls, and patterns that work
#DataScience #AI #Python
📌 AI Agents Need Their Own Desk, and Git Worktrees Give Them One
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-18 | ⏱️ Read time: 20 min read
Git worktrees, parallel agentic coding sessions, and the setup tax you should be aware of
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-18 | ⏱️ Read time: 20 min read
Git worktrees, parallel agentic coding sessions, and the setup tax you should be aware of
#DataScience #AI #Python
📌 How to Learn Python for Data Science Fast in 2026 (Without Wasting Time)
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-18 | ⏱️ Read time: 8 min read
What I wish I did at the beginning of my journey
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-18 | ⏱️ Read time: 8 min read
What I wish I did at the beginning of my journey
#DataScience #AI #Python
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