DBLX @codevelex

@aastha_mhaske Java

🚀 𝗝𝗮𝘃𝗮 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 A Practical Guide to Interview Preparation Master the core concepts you need to crack your next backend or microservices interview! ✅ Spring Boot & REST APIs ✅ Service Discovery & Load Balancing ✅ Docker, Kubernetes & CI/CD ✅ Real-world Interview Q&A 📘 FREE for the first 500 people! 1️⃣ Like & Repost 2️⃣ Comment “JAVA” 3️⃣ Follow (so I can DM you the guide) Level up your backend interview prep today! 💻 #Java #Microservices #BackendDevelopment #InterviewPrep #SpringBoot #Giveaway #Developers #Coding

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@brankopetric00 Thank you in advance. There's a guide, video or overview for this? It looks really interesting.

Buenos días @LenovoES , vuestro servicio de soporte es completamente inútil y con un trato al cliente lamentable. ¿Tenéis alguien en nómina que realmente sea capaz de ayudar a vuestros clientes? Necesito que me resolváis un problema. Gracias.

Hace un tiempo detectamos en uno de los servidores de mi empresa una serie de escaneos de puertos y decidimos montar una trampa divertida. Un Tarpit llamado Endlessh. github.com/skeeto/endlessh

Goodbye Mufasa….long live the King!

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Coding games are the best way to learn coding. From CSS, Python, JavaScript to Blockchain. Here are 10 of the BEST online games to learn coding:

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Explaining black box machine learning models is critical to gaining leadership's buy-in and trust. Here's 6 months of research on Explainable ML in 6 minutes (Business Case included). Let's go! 1. Explainable Machine Learning (ML): Refers to techniques that make the outputs and operations of machine learning models understandable to humans. Traditional machine learning models, especially complex ones like deep neural networks, are often seen as "black boxes" because their internal workings are not easily interpretable. 2. Black-Box Problem: People don't trust what they don't understand. It's that simple. With Explainable ML, you gain: Transparency, Interpretability, Accountability, Fairness and Bias Detection, and Trust. This builds confidence among stakeholders, which is especially important in domains like marketing, finance, and healthcare. 3. The 2 Types of Explainability Approaches: Model-Specific and Model Agnostic. Let's break them down. 4. Model-Specific Explainability: Some models are explainable without any added processing. These tend to be simpler models. Linear Regression Coefficients: In linear models, the coefficients indicate the importance and direction of the influence of each feature. Decision Tree Rules: Decision trees provide a clear set of rules and thresholds for decision-making, making them inherently interpretable. 5. Model-Agnostic Explainability: These are methods that can be applied to ANY model. Examples include Feature Importance Scores, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Partial Dependence Plots (PDP). 6. Why I use Explainable ML? In my $15,000,000 lead scoring model, it initially started out as a Linear/Logistic Regression. This was a simple model. But eventually I upgraded. I went to Random Forest, then XGBoost, then an Ensemble of multiple ML models. With each iteration, predictions (lead scores) became more accurate. But, I could no longer understand why the model was predicting (e.g. Unlike Linear Models, XGBoost has no coefficients). There you have it- my top 6 concepts on Explainable ML. The next problem you'll face is how to apply data science to business. I'd like to help. I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with: 300+ lines of R and Python code 5 bonus trainings 2 systematic frameworks 1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY. 👉 Here it is for free: learn.business-science.io/free-solve-bus…

Han reunido en un único repo 500 blogs sobre Ingeniería de Software ordenados alfabéticamente. github.com/kilimchoi/engi…

Guía de cómo deshacer el último commit con git Si no has hecho push: • Para conservar cambios: git reset –soft HEAD~1 • Para descartar cambios: git reset –hard HEAD~1 • Para modificar el commit (mensaje o agregar cambios): git commit –amend -m “Nuevo mensaje” Si ya has hecho push: • git revert para crear un nuevo commit que deshace los cambios. Si hay conflictos, prepárate para resolverlos. • git log para obtener el hash del commit que deseas revertir. Más avanzado: • git rebase -i para modificar tu historial de commits localmente. Puedes reorganizar, combinar, editar o eliminar commits. • Luego ejecuta git push –force-with-lease • ⚠️ Precaución: Sólo usa rebase y push forzado en ramas donde seas el único colaborador o en situaciones donde todos los colaboradores estén informados y de acuerdo con reescribir la historia.

Aprende los COMANDOS de LINUX mientras juegas en estos 5 sitios ↓

Writing a scientific article: A Step-by-Step Guide for Beginners (1/7)

This is how cows reproduce:

Automatiza tu código y desarrolla de forma más rápida y segura. He creado un tutorial de GitHub actions de 1 hora para aprender a usar esta herramienta gratis y desde cero. ¿Me ayudas a compartirlo para que más programadores lo descubran? ▶️ youtu.be/pNtcTmCiXzw

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GPT-4 Vision has a major competitor! And the best part, you can use it for FREE! Here's how it works: