VectoScalar Technologies

VectoScalar Family wishes you a very Happy Birthday Akhil Chaudhary!!! 😊 🎉 #Happybirthday #Birthdaywishes #VectoScalar #Keepgrowing

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VectoScalar Family wishes you a very Happy Birthday Khyati Chhajed!!! 😊 🎉 #Birthdaywishes #Happybirthday #Birthdaypost #VS

VectoScalar Family wishes you a very Happy Birthday Shubham Gupta!!! 😊 🎉 #Happybirthday #birthdaywishes #Keepgrowing #VS #vectoscalar

VectoScalar Family wishes you a very Happy Birthday ANIL KUMAR SINGH !!! 😊 🎉 #Happybirthday #Birthdaywishes #Vectoscalar #VS

VectoScalar Family wishes you a very Happy Birthday Praveen Verma!!! 😊 🎉 #Happybirthday #Birthdaywishes #Vectoscalar #VS

The ultimate cheat sheet for your LLM interview is here: Topics to Master:  1. Prompt Engineering Basics     Learn types, strategies, and stopping criteria.  2. Retrieval Augmented Generation (RAG)     Understand architecture, use cases, and accuracy boosts.  3. Internal Working     Transformers, attention mechanisms, and vector databases.  4. Advanced Concepts     Fine-tuning, hallucination control, and preference alignment.  5. Real Scenarios     Case studies, deployments, and cost optimization.  This guide has 100+ questions covering everything from basics to advanced topics.  🔗 Access the full guide here: https://lnkd.in/g7Uyv7G7 _____________  I share my learning journey here. Join me and let's grow together.  Enjoy this? Repost it to your network and follow Karn Singh for more.  

Here’s a breakdown of the top 10 algorithms that form the backbone of AI systems today: 1. Linear Regression  - Overview: A foundational algorithm for predicting continuous variables by finding relationships in data.     - Use Case: Predict housing prices.     - Evaluate With: Metrics like R-squared or Mean Squared Error (MSE). 2. Logistic Regression  - Overview: Used for binary classification problems like spam detection.     - Use Case: Classify emails as spam or not.     - Key Tip: Changing the threshold can significantly impact accuracy. 3. Decision Trees  - Overview: Powerful for both classification and regression, and easy to interpret.     - Use Case: Predict customer churn.     - Watch for: Overfitting when trees become too deep. 4. Random Forest  - Overview: Builds multiple decision trees to improve accuracy.     - Use Case: Improve customer churn predictions.     - Key Benefit: More robust than a single decision tree, it reduces overfitting. 5. Support Vector Machines (SVM)  - Overview: Effective for both classification and regression in high-dimensional spaces.     - Use Case: Classify handwritten digits.     - Experiment: Try different kernels (linear, RBF) to optimize results. 6. K-Nearest Neighbors (KNN)  - Overview: A simple algorithm that classifies data based on nearest neighbors.     - Use Case: Classify patients based on medical records.     - Key Insight: Choosing the right number of neighbors ('k') is essential. 7. Naive Bayes  - Overview: A family of probabilistic classifiers based on Bayes’ theorem, assuming feature independence.     - Use Case: Classify news articles.     - Pro Tip: Works well for text classification even if features aren’t fully independent. 8. K-Means Clustering     - Overview: Unsupervised learning used for clustering data into groups.     - Use Case: Segment customers by shopping habits.     - Pro Tip: Use the elbow method to determine the optimal number of clusters. 9. Principal Component Analysis (PCA)     - Overview: A dimensionality reduction technique that simplifies datasets.     - Use Case: Reduce the size of image datasets.     - Impact: PCA makes large datasets easier to process and visualize. 10. Gradient Boosting Machines (GBM)  - Overview: Builds models sequentially, where each corrects the errors of the previous one.     - Use Case: Improve accuracy in complex tasks.     - Fine-Tune: Adjust parameters like learning rate and number of trees for better performance. — I share my learning journey here. Join me and let's grow together. Enjoy this? Repost it to your network and follow @Karn Singh for more.

Ashish Poddar created an awesome AI assistant to learn Sanskrit.  Sanskrit might be ancient, but learning it doesn’t have to be.  I recently sat down with Ashish, a Product Leader at Microsoft, to talk about his AI-powered project: Sanskrit Tutor.  Sanskrit tutor is a modern, personal, interactive AI tutor that makes learning Sanskrit simple and fun.  Here’s what stood out:  - It’s perfect for Hindi speakers who want to dive into Sanskrit.   - Future plans? Tailored lessons for texts like the Bhagavad Gita!  Ashish is building a beta group to test this. → Reach out to him via DM to join, share feedback, or connect! Interested? Check the link below: https://lnkd.in/gkrr99uz _____________  I share my learning journey here. Join me and let's grow together.  Enjoy this? Repost it to your network and follow Karn Singh for more

New tools added to the DroneX AI Tech Stack!  These AI tools have seriously leveled up how we get things done:  1. Napkin AI   Turns data into pro visuals (PDFs, PNGs).   Perfect for reports, presentations, or videos—no designer needed.  2. Clay Automates sales: builds customer lists + sends personalized emails.   More leads, less hassle.  3. Notebook LM Converts articles into audio podcasts.   Ask questions, get answers—great for on-the-go learning.  4. CrewAI Automates repetitive tasks with drag-and-drop AI agents.   Think: your digital assistant.  5. Replicate  Run open-source AI models—no coding required.   Create images, automate workflows, and more.  6. @Autopod AI Podcast editing on autopilot.   Cuts frames, edits, and saves you hours of work.  __________________  I share my learning journey here. Join me and let's grow together.  Enjoy this? Repost it to your network and follow Karn Singh for more.