Solved Daily Temperatures problem using Monotonic Stack in Python

🚀 New Video: How to Build a RAG Model Step by Step Ever wondered how to make AI models more accurate by connecting them with real-world data? In this tutorial, you’ll learn how to build a Retrieval-Augmented Generation (RAG) system from scratch using Python. 🔍 What You’ll Learn: What RAG is and why it’s essential for modern AI How RAG retrieves external knowledge to enhance LLM responses Step-by-step RAG implementation in Python Building online (Gemini) and offline (local) versions Using LangChain, ChromaDB, Sentence Transformers, and Hugging Face models Running RAG efficiently with Google Colab or local setups 💻 Tools Used: Python | LangChain | ChromaDB | Gemini | Sentence Transformers | Hugging Face | Google Colab Perfect for AI beginners, data engineers, and developers exploring how to integrate external data with LLMs. Watch the full tutorial and start building smarter AI systems today! 🎥👇 https://lnkd.in/dnqg4EKB #AI #RAG #LangChain #Python #LLM #GenerativeAI #MachineLearning #HuggingFace #Gemini #NeuneWorks

🌟 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

🐍 𝐏𝐲𝐭𝐡𝐨𝐧: 𝐓𝐡𝐞𝐧, 𝐍𝐨𝐰 & 𝐅𝐨𝐫𝐞𝐯𝐞𝐫 This image perfectly captures the journey of Python’s unstoppable popularity: Then: Python, the rising star embraced by early enthusiasts. Now: The go-to language for AI, data science, automation, web and more. In Future: Still standing strong—because Python isn’t just a trend, it truly is a technology foundation. ✔️ Simple syntax ✔️ Massive community ✔️ Endless libraries ✔️ Friendly for beginners, powerful for pros No matter how the tech world changes, Python stays at the center—reliable, strong, and widely loved. 🚀 The present and the future both speak Python. #Python #PythonProgramming #Coding #AI #DataScience #Automation #MachineLearning #WebDevelopment #SoftwareEngineering #TechCareers #Programming #PythonCommunity #DevOps #OpenSource #PythonForever #TechLearning #100DaysOfCode #CodeNewbie

Excited to share my latest AI project — a PDF Summarization App powered by Gemini 2.5 Pro! The tool reads PDFs, processes the content, and generates summaries in multiple formats (short, medium, detailed, or bullet points). Built using Python, LangChain, and Streamlit, this project explores how LLMs can streamline document understanding and content generation. #AI #Gemini #LangChain #Python #LLM #Streamlit #GenerativeAI

🔹 Day 1 of 30 – LeetCode Challenge: Preorder Traversal of Binary Tree Today, I practiced a fundamental Tree Traversal problem using Python. The task was to return the preorder traversal of a binary tree’s nodes (Root → Left → Right). 🧩 Problem Statement: Given the root of a binary tree, return the preorder traversal of its nodes’ values. Example: Input: root = [1, null, 2, 3] Output: [1, 2, 3] 💡 Concept: In preorder traversal, we visit: Root node Left subtree Right subtree This can be solved either recursively or iteratively using a stack. I implemented the recursive approach, which is straightforward and elegant. 🕒 Complexity: Time Complexity: O(n) Space Complexity: O(h), where h = height of the tree 🏆 Result: ✅ All test cases passed 🚀 Runtime: 0 ms (Beats 100%) 💾 Memory: 12.41 MB (Beats 62.16%) 💬 Learning: Understanding tree traversal is a must for mastering recursion and data structures. This problem helped me revise depth-first traversal concepts efficiently. #30DaysOfCode #LeetCode #Python #DataStructures #Algorithms #BinaryTree #Recursion #MTech #CodingChallenge #PreorderTraversal

🚀 Level up your LLM game! Ever wished you could easily prep your documents for Large Language Models? 💡 This Real Python article introduces "Python MarkItDown," a handy tool that transforms PDFs, Office files, images, and URLs into clean, LLM-ready Markdown. You can do this in mere seconds! ✅ This is a game-changer because clean Markdown is crucial for optimal LLM performance. It ensures your models can understand and process your data effectively, leading to better results. 🔥 Imagine the possibilities for documentation, research, and data preparation. • No more manual formatting! • Save time and effort. • Improve LLM accuracy. 📖 Read more: https://lnkd.in/g8-DcaUF #Python #LLM #Markdown #ArtificialIntelligence #DataScience

Excited about Python 3.14's latest release! With the new free-threaded mode (bye-bye GIL!), we're unlocking true multi-core parallelism for CPU-bound tasks. This could be a game-changer for AI applications like RAG-based chatbots, where efficient chunk retrieval from vector databases is key to low-latency responses. Question for the community: How do you think leveraging Python 3.14's parallel CPU core usage could optimize the retrieval process in RAG systems—perhaps speeding up embedding searches or handling concurrent queries more effectively? Would love to hear your thoughts, experiments, or use cases below! 🚀 #Python #Python314 #AI #RAG #MachineLearning #DataScience #Concurrency #TechInnovation

Tech With Tim: Python Skills You NEED Before Machine Learning This video lays out a clear Python roadmap for anyone eyeing machine learning—starting with core syntax and data handling, moving through interactive tools and essential software-engineering practices, and even touching on optional math. From foundational ML concepts and deep learning to real-world projects and a bonus LLM section, it’s everything you need to level up your skills and build a killer portfolio. Plus, you get insider hooks on beginner-friendly Datacamp tracks (with a sweet 25% discount!) and an invite to DevLaunch, where you’ll get hands-on mentorship, real-world project guidance, and job-ready accountability—no fluff, just results. Watch on YouTube https://lnkd.in/gxZJi-Q9

🚀 Excited to share my latest technical note on feature scaling techniques in machine learning! In this resource, I break down the essential scaling methods — including Min–Max normalization, standard (z-score) scaling, and robust scaling. The highlight? Each concept comes with hands-on code examples and visualizations to help you see the impact on real data step-by-step. Whether you're building data pipelines, preparing features for modeling, or just curious about preprocessing best practices, this note will give you practical insights you can directly apply with Python. Let’s connect and discuss! I’d love your thoughts and feedback. #DataScience #MachineLearning #Preprocessing #Scaling #Python #Visualization #FeatureEngineering #EDATips