Artificial Intelligence

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 To get a Facebook Access Token and Facebook Page ID , you'll need to follow a series of steps. These are typically used for accessing Facebook's Graph API, allowing you to interact programmatically with Facebook services. Here's how you can get both: 1. Get Facebook Access Token An Access Token is used to authenticate your app and interact with Facebook's API. Here's how to get it: a. Create a Facebook Developer Account: Go to the Facebook Developer Console . Click Get Started and follow the prompts to create a developer account if you don’t have one already. b. Create a Facebook App: Once logged in, go to the Facebook Developer Dashboard . Click Create App . Select the type of app you want to create (usually For Everything Else or Business ). Follow the prompts to set up your app, entering basic information like app name, email, etc. c. Generate Access Token: After your app is created, you’ll be directed to your app's dashboard. In the left sidebar, go to ...

  Backpropagation in Deep Learning: A Journey of Self-Improvement At the core of Deep Learning and Neural Networks, there’s a powerful concept that mirrors the way we, as humans, improve ourselves: Backpropagation . Just like how we learn from our mistakes, adjust our approach, and move closer to our goals, backpropagation allows neural networks to update themselves iteratively to minimize errors and optimize performance. It's a beautiful process that combines mathematics with the pursuit of improvement—whether in machines or in our own lives. For anyone curious to dive deeper into the mechanics, I highly recommend checking out these resources: 1️⃣ Backpropagation Visualized by 3Blue1Brown: A fantastic breakdown of how backpropagation works in neural networks. Watch here 2️⃣ Backpropagation Explained by xnought: An intuitive, easy-to-follow explanation of how this learning process unfolds. Read here #3  Finding global minima visualization:  Parameter optimization in ne...

 GenAI Interview Question What are hallucinations in LLMs, why do they occur, and how do you detect and mitigate them in production systems? In a high-throughput production system, when would you choose a list over a dictionary (or vice versa), and why? In a performance-critical Python service, how do best- and worst-case list operations impact latency? Which similarity measures are used to compare embedding vectors, and how do you choose the right one for production RAG systems? How are embedding vectors stored and indexed inside a vector database, and what trade-offs affect speed, memory, and recall? What is re-ranking in a RAG pipeline, when should it be introduced, and what trade-offs does it create? How would you design a secure enterprise chatbot with role / pay-grade based access control? How is vector retrieval fundamentally different from SQL queries? What is LangGraph, how does it differ from LangChain, and how does agentic RAG change system design? In a production RAG sy...