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RBI Grade B 2025 How to Clear Reasoning Cut Off ?
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RBI Grade B 2025 How to Clear Reasoning Cut Off ?
You are invited to a Zoom meeting.
When: Sep 21, 2025 12:00 PM India
Join Now - https://zoom.us/meeting/register/Shu1PXJRS6GMStAz5M5gQQ
Workshop Details:
Date: Sunday, September 21st
Time: 12 PM
Mentors: Aakansha Agarwal (ixamBee Reasoning Faculty), CP Joshi (ex AGM RBI)
Don’t miss your chance to make General Awareness your strongest section.
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Welcome! You are invited to join a meeting: How to Clear Reasoning Cut Off ( By Aakansha Maam and CP Sir). After registering, you…
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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
Join in advance for this meeting.
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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.
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When: Sep 21, 2025 7PM India
Join in advance for this meeting.
https://zoom.us/meeting/register/BsdEWljCQQKmUP61hSWP4A
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.
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Target RBI Grade B 2025
Workshop at 7PM- RBI Grade B 2025 How to Clear Quant Cut Off ? You are invited to a Zoom meeting. When: Sep 21, 2025 7PM India Join in advance for this meeting. https://zoom.us/meeting/register/BsdEWljCQQKmUP61hSWP4A Workshop Details: Date: Sunday…
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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
Join in advance for this meeting.
https://zoom.us/meeting/register/BsdEWljCQQKmUP61hSWP4A
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.
RBI Grade B 2025 How to Clear Quant Cut Off ?
You are invited to a Zoom meeting.
When: Sep 21, 2025 7PM India
Join in advance for this meeting.
https://zoom.us/meeting/register/BsdEWljCQQKmUP61hSWP4A
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.
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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.
• 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.
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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.
• 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.
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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.
• 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.
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LLMs & Agentic AI
• Transformers: These are the models that power modern AI like ChatGPT. They are especially good at understanding the context of words, which is why they are so effective for tasks like language translation and generating human-like text.
• Embeddings: Imagine converting words or images into a language an AI can understand: numbers. Embeddings are these numerical representations, making it possible for the AI to process and find relationships between different types of data.
• Zero-shot Learning: This is when an AI solves a problem without being specifically trained for it. It can use its vast knowledge to figure out an answer or complete a task it has never seen before.
• Few-shot Learning: The AI is shown just a few examples of a new task and can then do it correctly. This is like learning a new game after watching just a few rounds.
Supervised Learning
• Supervised Learning: This is like a student learning with a teacher. The AI is given data with correct answers (labels), and it learns to find patterns to predict the right answer on its own.
• Support Vector Machines (SVM): Imagine a boundary line that separates different groups of data. SVM is an algorithm that tries to find the best possible boundary to separate the groups with the biggest gap in between them.
• Feature Scaling: This is like making all the ingredients in a recipe the same size. Feature scaling ensures all the inputs in a model have a similar range, so one feature doesn’t dominate over another just because it has a larger numerical value.
• Bounding Box: This is a simple rectangle or box drawn around an object in an image to show its location and size, used for tasks like object detection.
• Transformers: These are the models that power modern AI like ChatGPT. They are especially good at understanding the context of words, which is why they are so effective for tasks like language translation and generating human-like text.
• Embeddings: Imagine converting words or images into a language an AI can understand: numbers. Embeddings are these numerical representations, making it possible for the AI to process and find relationships between different types of data.
• Zero-shot Learning: This is when an AI solves a problem without being specifically trained for it. It can use its vast knowledge to figure out an answer or complete a task it has never seen before.
• Few-shot Learning: The AI is shown just a few examples of a new task and can then do it correctly. This is like learning a new game after watching just a few rounds.
Supervised Learning
• Supervised Learning: This is like a student learning with a teacher. The AI is given data with correct answers (labels), and it learns to find patterns to predict the right answer on its own.
• Support Vector Machines (SVM): Imagine a boundary line that separates different groups of data. SVM is an algorithm that tries to find the best possible boundary to separate the groups with the biggest gap in between them.
• Feature Scaling: This is like making all the ingredients in a recipe the same size. Feature scaling ensures all the inputs in a model have a similar range, so one feature doesn’t dominate over another just because it has a larger numerical value.
• Bounding Box: This is a simple rectangle or box drawn around an object in an image to show its location and size, used for tasks like object detection.
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Unsupervised Learning
• Unsupervised Learning: This is like learning without a teacher. The AI is given data without any correct answers and has to find hidden patterns or groups on its own.
• K-Means Clustering: An algorithm that sorts data into groups based on their similarities. It's like a teacher telling students to form groups with others who have similar hobbies without telling them what those groups are.
• Dimensionality Reduction: This is the process of simplifying data by reducing the number of features or variables while keeping the most essential information. It's like summarizing a long paragraph to get the main idea.
• Optical Character Recognition (OCR): This is technology that "reads" text from images or scanned documents. It converts the text in the image into a digital format that can be edited and searched.
Reinforcement Learning
• Reinforcement Learning: The AI learns by trying things out and getting a reward for good actions or a penalty for bad ones, like a dog learning tricks with treats. It's a "trial and error" method.
• Naive Bayes: A simple but powerful algorithm that works surprisingly well for tasks like classifying text or emails. It's based on probability and assumes that features are independent of each other.
• Overfitting: This happens when an AI learns the training data so well that it fails on new, unseen data. It's like a student who memorizes test answers but doesn't understand the concepts and fails on a slightly different test.
• Generative Adversarial Networks (GANs): A system of two AIs working against each other. One creates fake images or videos, and the other tries to spot the fakes. This process helps the generator create very realistic-looking content.
Transfer Learning
• Transfer Learning: Reusing knowledge from a previously trained AI model to help with a new, related task. It’s like a person who already knows how to play the guitar learning the ukulele much faster because of their existing skills.
• Principal Component Analysis (PCA): A way to shrink large datasets while keeping the main patterns. It simplifies complex information, making it easier for AI models to work with.
• Data Augmentation: Boosting an AI's learning by creating slightly altered copies of existing data, like rotating or flipping an image. This helps the AI learn more effectively without needing to collect more data.
• Vision Transformers (ViTs): A new kind of AI model for images. Unlike older models, ViTs look at images in a way similar to how they process text, which has made them very powerful for computer vision tasks.
• Unsupervised Learning: This is like learning without a teacher. The AI is given data without any correct answers and has to find hidden patterns or groups on its own.
• K-Means Clustering: An algorithm that sorts data into groups based on their similarities. It's like a teacher telling students to form groups with others who have similar hobbies without telling them what those groups are.
• Dimensionality Reduction: This is the process of simplifying data by reducing the number of features or variables while keeping the most essential information. It's like summarizing a long paragraph to get the main idea.
• Optical Character Recognition (OCR): This is technology that "reads" text from images or scanned documents. It converts the text in the image into a digital format that can be edited and searched.
Reinforcement Learning
• Reinforcement Learning: The AI learns by trying things out and getting a reward for good actions or a penalty for bad ones, like a dog learning tricks with treats. It's a "trial and error" method.
• Naive Bayes: A simple but powerful algorithm that works surprisingly well for tasks like classifying text or emails. It's based on probability and assumes that features are independent of each other.
• Overfitting: This happens when an AI learns the training data so well that it fails on new, unseen data. It's like a student who memorizes test answers but doesn't understand the concepts and fails on a slightly different test.
• Generative Adversarial Networks (GANs): A system of two AIs working against each other. One creates fake images or videos, and the other tries to spot the fakes. This process helps the generator create very realistic-looking content.
Transfer Learning
• Transfer Learning: Reusing knowledge from a previously trained AI model to help with a new, related task. It’s like a person who already knows how to play the guitar learning the ukulele much faster because of their existing skills.
• Principal Component Analysis (PCA): A way to shrink large datasets while keeping the main patterns. It simplifies complex information, making it easier for AI models to work with.
• Data Augmentation: Boosting an AI's learning by creating slightly altered copies of existing data, like rotating or flipping an image. This helps the AI learn more effectively without needing to collect more data.
• Vision Transformers (ViTs): A new kind of AI model for images. Unlike older models, ViTs look at images in a way similar to how they process text, which has made them very powerful for computer vision tasks.
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A lot of people are asking me about RBI Grade A. Some current RBI Grade A officers told me that there is some negative news about it, but I haven’t received that news yet.
World is dynamic, different leaders may have different priorities.
Many times, people only want to hear positive news. If there is something negative, they lose their temper and criticise the person sharing it. That’s why I have to think twice before passing on such news.
Once again, I want to repeat that I don’t have any news yet, and probably I won’t even try to find it. The future is always uncertain, so don’t skip the RBI Grade B Notification and wait for something extraordinary. Focus on what is available right now.
Please don’t ask me on various social media,I already have 500+ pending messages. If I can’t reply to everyone, some people may think negatively about me. But honestly, it’s just impossible to reply to all.
World is dynamic, different leaders may have different priorities.
Many times, people only want to hear positive news. If there is something negative, they lose their temper and criticise the person sharing it. That’s why I have to think twice before passing on such news.
Once again, I want to repeat that I don’t have any news yet, and probably I won’t even try to find it. The future is always uncertain, so don’t skip the RBI Grade B Notification and wait for something extraordinary. Focus on what is available right now.
Please don’t ask me on various social media,I already have 500+ pending messages. If I can’t reply to everyone, some people may think negatively about me. But honestly, it’s just impossible to reply to all.
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