In engineering colleges across the country, final year projects are often treated as just another academic task. But what many students fail to realize is this: Your final year project isn’t just a submission. It’s your first real-world proof of work. And that’s exactly why original work in your final…
Choosing the right final year project is one of the most important decisions of your engineering journey. It’s more than just a submission — it’s a reflection of your interests, skills, and future goals. Unfortunately, many students pick projects based on what their friends are doing or what’s available last…
GitHub: https://github.com/nishantmunjal2003/mcp-server-gemini 📌 Project Overview MCP Server is a lightweight, extendable API server that: ⚙️ Features 📁 Folder Structure bashCopyEditmcp-server/ │ ├── app.py # Main FastAPI server ├── gemini_integration.py # Gemini API integration ├── schema.sql # SQL for DB setup ├── requirements.txt # Python dependencies ├── .env # Environment variables…
Ever caught yourself reaching for your phone, telling yourself it’s “just for a minute”… and then suddenly it’s midnight?You didn’t mean to spend the last hour watching cat videos, political debates, or oddly satisfying cleaning reels. But somehow, you did. Again. Don’t worry — it’s not just you.It’s your brain….
Describe the scene using three vivid sensory details — one for sight, one for sound, and one for touch. Summarize the mood of the image in one powerful sentence without naming any objects. Write a poetic description of the image focusing only on colors and emotions. Imagine the main subject…
Dimensionality reduction is the process of reducing the number of features (variables) in a dataset while preserving important information. It helps in: ✅ Reducing computational cost (faster processing)✅ Removing noise from data✅ Avoiding overfitting✅ Visualizing high-dimensional data Types of Dimensionality Reduction 1. Feature Selection (Selecting important features) 2. Feature Extraction…
The tanh function, short for hyperbolic tangent function, is another commonly used activation function in neural networks. It maps any real-valued number into a value between -1 and 1. This function is similar to the sigmoid function but offers some advantages that make it more suitable for certain applications. Mathematical…
Initializing weights and biases is a crucial step in building a neural network. Proper initialization helps ensure that the network converges to a good solution and does so efficiently. Let’s explore the reasons in detail: 1. Breaking Symmetry If all weights are initialized to the same value (e.g., zeros), then…
The sigmoid function is one of the most commonly used activation functions in neural networks, especially in binary classification tasks. It maps any real-valued number into a value between 0 and 1, making it suitable for models that need to output probabilities. Mathematical Definition The sigmoid function, also known as…