How Python 3.14's parallelism can boost RAG systems

Wouldn't it be nice if AI could ELI5 your detections? Claude Haiku 4.5 can do it fast and with viz. This example has complex detection code (Python) explained, rendered as a flow chart and then includes a summary of the output alert. The new Haiku makes it practical to embed AI deep into your application due to the speed and low cost.

🌟 New Blog Just Published! 🌟 📌 Python's GIL Future: What's Next for Multithreading? 🚀 ✍️ Author: Hiren Dave 📖 Python has become the lingua franca for everything from rapid prototyping and mission-critical backend services processing billions of requests, to powering the largest AI/ML training clusters. This..... 🕒 Published: 2025-11-11 📂 Category: Tech 🔗 Read more: https://lnkd.in/dK-F8YQ4 🚀✨ #pythongil #multithreadingpyth #gilfuture

Hey everyone! 👋 I recently tried something fun and futuristic — a Python project that lets you control your mouse using your eyes! 👀🖱️ With the help of OpenCV, Mediapipe, and PyAutoGUI, I built a system that tracks eye movement to move the cursor and even detects blinks to perform clicks. No mouse, no touchpad… just your eyes doing all the work! 😄 💡 Why I made this project: To explore how we can make computers hands-free and more accessible. To build something that’s both fun and useful. To see how AI and Computer Vision can help people interact with tech in new ways. ⚙️ Tech Used: Python | OpenCV | Mediapipe | PyAutoGUI #EyeControlMouse #AIProjects #ComputerVision #MachineLearning #TechInnovation #AssistiveTechnology #HumanComputerInteraction #GestureControl #SmartTechnology

First-year CS has a common plotline: Week 1: “I’ll learn C++, Python, AI… all this month.” Result: 30 tabs open, zero depth. Week 2: Quick cram → decent quiz → memory wiped by Friday. Week 3: Scroll, compare, panic. Someone shipped an AI app. You’re still on loops. Focus > FOMO. Pick one language for 30–90 days. Go deep. Cramming ≠ learning. Build tiny projects. Break them. Fix them. Consistency beats competition. Your day-1 isn’t their year-2. Simple plan that works: 1 small project a week • 30–60 mins a day • Share a Sunday demo Drop in the comments with your one focus for the next 30 days ! #CS #codingjourney #learnbybuilding #studentlife #vedamschooloftechnology #schooloftechnology #1styear

🍂 Experiment 8: Logistic Regression using Python ⚙️ In this lab, I explored Logistic Regression, a fundamental algorithm for binary classification problems in machine learning. 🔍 Key learning outcomes: • Understanding the concept of logistic (sigmoid) function and decision boundaries • Implementing Logistic Regression using scikit-learn • Visualizing classification results and interpreting probabilities This experiment strengthened my grasp of classification techniques and how Logistic Regression forms the foundation for many real-world applications like spam detection, disease prediction, and customer segmentation. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience  #MachineLearning  #Python  #LogisticRegression  #ScikitLearn  #Classification #PredictiveAnalytics  #LearningJourney  #JupyterNotebook Ashish Sawant Sir

Mestrelab Research S.L. has released the final episode of the #Mnova Scripting with Python series! From data extraction to AI-powered classification, this series walks you through how to automate analytical workflows using Python inside Mnova. In the final episode, everything comes together, building and training an AI classifier to detect aromatic substitution patterns directly from NMR spectra, using TensorFlow, scikit-learn, and the Mnova API. 🎥 Catch up on the full video series and download the scripts to explore how Python scripting can boost productivity, reproducibility, and insight in your lab. 👉 Watch the complete series: https://lnkd.in/epKXNpzc #SciY #Mnova #Python #NMR #MachineLearning #AI #LabAutomation #DigitalScience

Continuing my journey through my 𝐌𝐏𝐡𝐢𝐥 𝐀𝐈 Machine Learning course with a hands-on implementation of 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧.⁣⁣ ⁣⁣ The goal was to build a binary classifier from scratch. I implemented the core components in pure Python, focusing on:⁣⁣ ① 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬: Using the Sigmoid function to transform a linear output into a probability (a value between 0 and 1).⁣⁣ ② 𝐂𝐨𝐬𝐭 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧: Implementing Log Loss (Binary Cross-Entropy) to measure the performance of the probability predictions.⁣⁣ ③ 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫: Applying Gradient Descent to find the optimal parameters by minimizing this new cost function.⁣⁣ ⁣⁣ It’s a great lesson in how a few core mathematical concepts can be combined to build a powerful classification model from the ground up. On to the next topic! 🚀⁣⁣ ⁣⁣ #ArtificialIntelligence #MachineLearning #LogisticRegression #Python #DataScience #FromScratch #MPhil #Classification #SigmoidFunction