KCD Pune’s Post

Today is #day13 of #100daysofcodechallenge 1. Today I have completed the week 7.3 of @100xdevs Cohort 2 by Harkirat Singh Practiced on Recoil and Context API 2. DSA- Learned more of OOPS Learned Abstract classes and interfaces Managing Dev and DSA is hectic but important #100xdevs #webdevelopment #opensource #learninpublic

I had an amazing experience participating in the Epoch Hackathon at IIIT Delhi! Our problem statement revolved around Efficient And Fair Line Construction for a set of houses with coordinates given by latitude and longitude. We had to design algorithms for efficient line construction, fair line construction, and multiple efficient lines using the California Housing Dataset provided by 'sklearn'. It was a challenging yet rewarding journey, and I'm proud of our team's efforts in tackling these complex objectives. Looking forward to more such opportunities in the future! #EpochHackathon #IIITDelhi #AlgorithmDesign #DataScience"

Participating in the Tri-NIT hackathon was an absolutely amazing experience! Over 4000 participants, divided into 4 tracks, competed in this nationwide hackathon conducted by ACM, Association for Computing Machinery Student Chapter of National Institute of Technology Karnataka, CSEA NIT Warangal and SPIDER R&D from National Institute of Technology, Tiruchirappalli, hosted by Pranav Sai Marella. My team, including Issac Zerubbabel, Pranav K and I, chose the Machine Learning track and worked tirelessly for 36 hours to design a deep learning model and a mobile application that detects cracks on roads in Realtime. We initially began by attempting to train an R-CNN model. After 28 hours of trying, we realized that this was getting us nowhere. We then switched to YOLO V8, an object detection model. In the short time period, we learnt to fine tune the YOLO V8 algorithm on the RDD2022_India dataset. 4 hours before deadline, this was successful. In addition to the model, we created a server for inference and a client for real-time detection of roadblocks. The mobile-friendly PWA includes a camera for instant model inferences, capturing images (and could also collect location coordinates) upon crack detection. Although we couldn't finish configuring the client to handle the response and demo on real roads, we presented the app, and then presented the model's results by directly sending requests to the server from the postman app. This hackathon presented challenges, but the skills we gained and applied made it a rewarding experience for our team. Watch our submission video here: https://lnkd.in/gWUdF8Nt Contribute to the Road Crack Detection project on our GitHub repository: https://lnkd.in/gjd5j_wz The below video shows clips from the hackathon. The last part shows when we found out we didn't make it to the finals. #Hackathon #MachineLearning #DeepLearning #YOLOV8

Here’s your final LinkedIn post for Day 2: 🚀 Day 2 of #GeeksforGeeks DSA 80 Days Challenge! 🚀 Continuing my journey with the GeeksforGeeks DSA 80 Days Challenge, and today’s problem was both interesting and fun to solve! Here’s what I worked on today: 🔍 Problem: Move all zeros in a given array to the end, in place. 💡 Solution Approach: The solution involves an efficient in-place technique: Traverse the array while maintaining a count to track the position for non-zero elements. Each time a non-zero element is encountered, place it at the count index and increment count. Once all non-zero elements are in place, fill the remaining positions with zeros. This method ensures minimal operations with optimal space usage. ✅ Time Complexity: O(n) ✅ Space Complexity: O(1) Check out my solution in the screenshot below! 👇 Looking forward to more exciting challenges as I sharpen my DSA skills! 💪 #GeeksforGeeks #gfg160 #GeekStreak2024 #DSA #CodingJourney #LearningEveryDay #DataStructures #Algorithms #TechGrowth

"Excited to share that our team, including Satvik Singhal, Satyaprakash Sahoo, and myself, clinched the second spot in the Machine Learning Hackathon hosted by IIT Hyderabad Tech! 🥈🚀 It was an intense competition, and we're thrilled to have had the opportunity to showcase our skills alongside talented participants. Huge thanks to the organizers and congratulations to all the participants! Looking forward to more such challenges ahead. #MachineLearning #Hackathon #IITHyderabadTech"

Day 8 of 160 Days of DSA Challenge! 🚀🔥 Each day in this journey brings a new challenge, and I’m loving every moment of pushing myself to think smarter and code better. 💡💻 Today’s problem: Stock Buy and Sell – Max One Transaction Allowed 📝 Problem Statement: Given an array prices[] representing stock prices on different days, the task is to determine the maximum profit possible with at most one transaction (1 buy + 1 sell). If no profit is possible, return 0. ⚡ Key Note: You must buy the stock before you sell it. 🔍 Example Input: prices[] = [7, 10, 1, 3, 6, 9, 2] 📈 Output: 8 💡 Explanation: Buy on Day 2 (price = 1) and sell on Day 5 (price = 9), yielding a maximum profit of 8. This problem helped reinforce essential concepts in array traversal and greedy algorithms, and it’s a great reminder of how to approach real-world optimization problems. 🎯 A big shoutout to GeeksForGeeks for their amazing resources and challenges that keep my learning on track. 🙌 Let’s keep the momentum going—one step closer to DSA mastery every day! 💪 #gfg160 #geeksstreak2024 #DSA #GeeksForGeeks

torchtune v0.2 is here! Details: https://lnkd.in/gAshfwcZ Code: https://lnkd.in/gEgETe5t We've added a number of heavily-requested features, integrated the latest goodness from across PyTorch and received some amazing contributions from our wonderful community! Some Highlights: - Sample Packing with Masking. Improve training speed by packing multiple samples in the same instance. Unlike other implementations, we paid special attention to masking needed to prevent cross-sample contanimation. - Distributed QLoRA w/ FSDP2. Train 70B models on multiple GPUs with QLoRA and torch compile! FSDP2 integration gave us ~12% improvement in tokens/sec and ~3.2x improvement in model initialization by unblocking init on "meta" device. - Quantization-aware Training w/ torchao. Quantization typically degrades model quality. In our experiments, QAT achieved ~17% lower perplexity without regressing on-device inference speed for Llama3-8B using ExecuTorch. Checkout the release notes for more details, including specifics of models like CodeLlama and Phi3. A special thank you to the torchtune community for all their contributions, feedback and suggestions. It's been a ton of fun hanging out with you all on Discord!

FriendliAI at #Chatbotconference, come visit us today and learn how to cut your GPU cost! 👾🤖🤑 #inference #finetuning #chatbot #llm #FriendliAI

🌟 Day 12: #geekstreak2024 Challenge 🌟 🔍 Problem Solved Today: Maximum Circular Subarray Sum 📌 Platform: GeeksforGeeks 🎯 Difficulty: Hard Key Takeaways: Gained insights into handling circular arrays by applying Kadane's algorithm to compute both the maximum subarray and the minimum subarray sum. Understood the use of total array sum for determining circular subarray cases. 📊 Results: ✅ Test Cases Passed: 1112/1112 ⏳ Time Taken: 0.22 seconds 🏆 Points Scored: 8/8 Loving the journey of mastering DSA concepts day by day. Let's keep pushing forward! 💪 #CodingChallenge #DSA #CircularArray #gfg160 #geekstreak2024 Thank You GeeksforGeeks

This Y-Hat trophy (for non-math geeks, Y-hat is a mathematical symbol for prediction) was 3D printed by my son. It's a prize for a prediction competition I created for my department. Sometimes the best professional development opportunities are based on real-world problems, competition and collaboration. They cost little, teach a lot and are a wonderful team-bonding exercise. #datageek #analytics #stem