Enok Learn’s Post

Day 17 of #6Companies30Days Challenge Today I solved the following questions:- 1. Airplane Seat Assignment Probability 2. Short Encoding of Words 3. Longest Word in Dictionary through Deleting Arsh Goyal #ReviseWithArsh #connections GitHub repo link-

Here is a curated collection of articles and resources to guide you through every step of a machine-learning project, from start to finish: https://lnkd.in/dMX_De7C

Namaskar Sabhi ko!! As I was looking through some machine write ups, I observed that most of them are so complicated that if I a new person in the field tries to solve any machine referring to the write ups, he gets even more confused than before. They can not understand the terminologies used in the blog, then end up googling it. In that context I thought why not make it easier for the people, so I decided I am going to make write ups of as many as possible machines in the easiest way possible in every expect. Here's the github link of the walk through of the first machine, I know you saw this one coming... MrRobot : https://lnkd.in/dNn4sJRt

Strategy Pattern The Strategy Pattern is a powerful tool for making code more flexible, extensible, and maintainable. By enabling the selection of an algorithm at runtime, this pattern allows for easy changes to behavior and the addition of new features. In my example project, we've used the Strategy Pattern to create different types of customer accounts, making it easy to adapt to new requirements without modifying the code. The ICustomerAccountCreationStrategy<T, C> interface provides a common method for creating a customer account, and the Strategy Pattern promotes the open/closed principle, making our code more robust. Check out my example implementation on GitHub and let us know what you think! #StrategyPattern #BehavioralDesignPattern #SoftwareDevelopment

Another day, another improvement to the Twingate Kubernetes Operator. Version 0.10.0 of the Twingate Kubernetes Operator is now available! So, what does it do? You can now expose K8s Service objects as Twingate Resources just by annotating - no need to manually create a TwingateResource. It's also now possible to import Twingate Resources created elsewhere to be managed by our Operator, making it easier to migrate from another tool over to Kubernetes. Check out the release for full details ➡ https://lnkd.in/gHks3a_r Want more info on getting started with the Twingate Kubernetes Operator? We've got you covered with our quick start guide ➡ https://lnkd.in/get63zfq

I'm absolutely thrilled to be diving into PyTorch on my local machine! It's truly incredible. Working locally gives you so much more control over your projects—you can create virtual environments to manage everything, set requirements to control the versions of each library, and so on. And guess what? I just implemented a simple ANN using PyTorch, and it's blowing my mind! Just imagine: one input layer, two dense layers, and the output layer—it's like magic! If you're curious, check out the code on GitHub. https://lnkd.in/gHA6YSae

🚀 Top 7 DSA Questions of the Week! 🚀 Excited to share that I've successfully tackled some challenging DSA problems this week!. I crafted these algorithms & enriching my learning journey. Delve into each DSA challenge : Q1 : Optimized Best stock trading for maximum profit. Q2 : Equally distributed chocolates with minimal packet differences. Q3 : Seamlessly inserted intervals into non-overlapping lists. Q4 : Efficiently merged overlapping intervals. Q5 : Maximized water containment between vertical lines. Q6 : Identified non-overlapping intervals for maximum coverage. Q7 : TrappingRainWater: Quantified trapped water in a histogram. Each solution reflects a deepened understanding of algorithmic strategies and problem-solving skills. Please check out my GitHub repository for the implementations of these DSA solutions. Repo : https://lnkd.in/dkvzQbvb Your feedback and contributions are highly appreciated! Let's collaborate and grow together. 🚀

Added support for OpenTofu in Terragrunt, by default, will be wrapped terraform command with a fallback to tofu. https://lnkd.in/dDnhUVhR

🌟 Exciting Announcement! 🌟 🔍 Searching for some inspiration? Look no further! I've just completed an engaging console program that's bound to lift your spirits or deepen your thoughts based on your mood. 🎭 Introducing my latest project: The Random Quote Generator! 📜 Here's how it works: You simply share your current emotions from a selection list, and voilà! The program serves up a quote tailored to counter or complement that feeling. Feeling sad? Get ready for a pick-me-up! Feeling happy? Brace yourself for some thought-provoking insight! 😊 But that's not all! To ensure a seamless experience, I've implemented various design patterns, adding an extra layer of efficiency and elegance to the system. Plus, there's built-in backup functionality, because reliability is key! 🔒 And the cherry on top? This project was built using CMake, streamlining the build process and making it easier to manage dependencies. 🛠️ As an added challenge, I've implemented a multi-threaded approach with one thread for sending requests and another for receiving responses. And for a touch of humor (and bonus points!), I've even made sure the sender is a worthy individual before delivering the response. 😄👌 Curious to see it in action? Check out the class diagram and a detailed video explanation of the system on my LinkedIn profile. Trust me, you won't want to miss it! 💡 GitHub : https://lnkd.in/dRFvZUQE #RandomQuoteGenerator #Innovation #DesignPatterns #MultiThreadedProgramming #CMake #CodeChallenge

Hey everyone, I just finished building book recommendation system using collaborative filtering! 🚀 Unlike my previous project, which was content-based, this one takes a different approach, leveraging pandas, numpy, sklearn, and flask. Check it out on GitHub: https://lnkd.in/gVSRzKPR P.S. - Planning to make a hybrid one soon which will use both content based and collaborative filtering and some more. #CollaborativeFiltering #MachineLearning #Flask