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EMRS Notification 2025
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RBI Grade B 2025 How to Clear Reasoning Cut Off ? Workshop starting in 20 Minutes!!

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When: Sep 21, 2025 12:00 PM India

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Date: Sunday, September 21st
Time: 12 PM
Mentors: Aakansha Agarwal (ixamBee Reasoning Faculty), CP Joshi (ex AGM RBI)
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Workshop at 7PM- RBI Grade B 2025 How to Clear Quant Cut Off ? 

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When: Sep 21, 2025 7PM India

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Date: Sunday, September 21st
Time: 7 PM
Mentors: Neha Arora (ixamBee Quant Faculty), Susheel Ragade (ex-Manager RBI)

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Workshop Started - Join Now

Workshop STARTED - JOIN NOW

RBI Grade B 2025 How to Clear Quant Cut Off ?

You are invited to a Zoom meeting.
When: Sep 21, 2025 7PM India

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Workshop Details:
Date: Sunday, September 21st
Time: 7 PM
Mentors: Neha Arora (ixamBee Quant Faculty), Susheel Ragade (ex-Manager RBI)

Don’t miss your chance to make quant your strongest section.

Basics
• Weights & Biases: Imagine a brain learning. Weights are the strength of connections between its "neurons" (data points), and biases are an extra push or pull, helping the AI make better decisions. They adjust to get the best answers.
• Layer: An AI model is like a factory assembly line. A layer is one stage on that line where the data is processed, analyzed, and passed on to the next stage.
• Backpropagation: This is how an AI learns from its mistakes. It's like getting an exam back and correcting your wrong answers by adjusting your understanding, which helps you do better next time.
• Gradient Descent: Think of yourself on a hill trying to find the lowest point in a fog. You take small steps downhill. Gradient descent is the method an AI uses to adjust its weights and biases to find the lowest "error" point.

Classical Algorithms
• Linear Regression: It's like drawing a straight line through a bunch of points on a graph to predict where the next point will be, like predicting house prices based on size.
• Logistic Regression: This is used for "yes/no" or "cat/dog" type questions. It uses a formula to predict the probability of something belonging to a certain category.
• Decision Trees: This is like a flowchart. The AI makes a series of decisions, or splits, based on certain rules to get to a final answer, like deciding if an email is spam or not.
• Random Forests: Instead of one decision tree, a random forest is like a committee of many different decision trees. They all vote on the answer, and the most common vote wins, making the result more accurate.

Data & Features
• Training Data: This is the material the AI studies to learn. It's like the textbook a student uses to prepare for an exam. The AI learns from this data to find patterns and make predictions.
• Test Data: This is like a final exam for the AI. It's data the AI has never seen before. We use it to check how well the AI has learned and how accurately it can predict new outcomes.
• Validation Data: A practice test for the AI before the final exam. It's used by engineers to fine-tune the AI's settings and make sure it doesn't just memorize the training data.
• Feature Engineering: Features are the most important ingredients for an AI model. Feature engineering is the process of selecting and preparing these ingredients from the raw data to make the AI model work best.
Computer Vision
• Image Classification: This is the process of identifying what's in an image. An AI can be trained to look at a picture and say, "That's a cat" or "That's a dog."
• Object Detection: This is more advanced than classification. The AI not only identifies what objects are in an image but also draws a box around each one, pinpointing its location.
• Semantic Segmentation: This is like coloring a picture with a specific purpose. The AI colors different parts of an image to show what each pixel belongs to, for example, coloring all the "road" pixels blue and all the "car" pixels red.
• Convolutional Neural Network (CNN): A special type of AI model designed for image tasks. It's great at recognizing patterns and shapes in images, which is why it's used for image classification and object detection.