Deep Learning

Bidirectional Encoder Representations from Transformers (BERT) Encoder Easy: Imagine you have a magical book that can understand any language spoken in the world. Not only does it understand, but it also remembers everything you’ve ever read in it. This magical book is like BERT, but instead of a physical book, it’s a tool used by computers to understand and remember text. Now, suppose you want to ask this magical book about a word or a sentence. Instead of just telling you what it means, it gives you a whole story or explanation that helps you understand why it’s important or how it fits into the world. That’s exactly what BERT does with words and sentences — it understands them deeply and provides rich explanations. But here’s the cool part: BERT doesn’t just do this for one person at a time. It can help millions of people at once by analyzing lots of texts and learning from them. This way, it gets smarter over time and can give better answers to questions about text. So, a BERT enco...

What is the actual meaning of a Constructor Method ? Easy: Imagine you’re building a LEGO house. Before you start adding walls, windows, and roofs, you first decide on the type of house you want to build. Maybe it’s a small cottage, a tall tower, or a spacious mansion. This decision is like choosing a constructor method in programming. In programming, especially in deep learning, a constructor method is like the blueprint for creating something new. Just like how you choose a LEGO set to determine what your house will look like, a constructor method tells a program how to create a new object or instance of something. This could be anything from a simple shape to a complex model like a neural network. For example, let’s say you’re building a robot. First, you need to decide what kind of robot you want to make — maybe a robot that can walk, talk, or even fly. Each of these robots would have different parts and abilities. In programming, you would use a constructor method to tell the ...

What is the actual meaning of Flattening a Tensor ? Easy: Imagine you have a big box of toys that you want to play with. But instead of having all your toys in one big box, they’re organized into smaller boxes inside the bigger box. Each smaller box might contain cars, dolls, action figures, etc., just like how each toy is part of a category. Now, imagine if you wanted to quickly find a specific car among all your toys. It would take time because you’d have to open every single smaller box to look for it. This is similar to what happens when computers try to find specific pieces of information (like a particular image or sound) in a “tensor” that’s structured like our toy boxes. A tensor is like a very special kind of box that can hold numbers or other data, and sometimes these numbers are organized in a way that makes it hard for the computer to quickly find what it needs. So, what we do is we “flatten” the tensor, which means we take all those smaller boxes out and put everything...

Self Attention Mechanism Easy: Imagine you’re at a big party with lots of friends, and everyone wants to talk to you at once It can be really hard to listen to one friend when another is shouting in your ear. But what if you had superpowers that allowed you to focus on one friend’s voice at a time, even though they’re all talking? You could choose who to listen to based on what you want to hear or who you want to talk to next. This is similar to how the Self-Attention mechanism works in deep learning. Deep learning is like teaching a computer to understand and respond to the world around us, just like how we understand and interact with our friends. However, computers don’t naturally understand language or images the way humans do. They need help figuring out which parts of a sentence or image are important for understanding the whole thing. The Self-Attention mechanism is like those superpowers for computers. It helps the computer decide which parts of an image or sentence are most im...

Optional Spectral Normalisation Easy: Let’s imagine you’re playing with a magic wand that can do two special tricks: it can make anything invisible, and it can make anything visible again. Now, suppose you’re trying to play a game where you need to find hidden objects, but sometimes the objects are too shiny or too dim, making them harder to see. The magic wand can help you adjust the “brightness” of the objects so that they’re always just right for finding them. Optional Spectral Normalization in deep learning is a bit like having this magic wand, but instead of adjusting the visibility of objects, it helps the computer understand and work with numbers in a special way that makes learning easier. Here’s how it works: Making Numbers Understandable: Just like how the magic wand can make objects invisible or visible, Optional Spectral Normalization helps the computer adjust the “scale” of numbers it uses to represent images or sounds. This adjustment makes it easier for the computer to ...

Instance Normalisation Easy: Imagine you are in a drawing class with your friends, and everyone is drawing a picture of an animal. Some of your friends are really good at drawing and make super-detailed and colorful pictures, while others are just learning and draw simpler, less vibrant images. Now, your teacher wants to hang all the drawings on a wall for everyone to see. But there’s a problem: the super-detailed drawings are very bright and stand out a lot, making the simpler drawings look too dim in comparison. To make it fair and so that all drawings look nice together, the teacher decides to adjust the colors in each drawing. For each drawing, the teacher looks at the colors in all the drawings and makes sure the bright colors aren’t too bright, and the dim colors aren’t too dim. He does this by comparing each color in a drawing to the average color of all the drawings. If a color is much brighter or dimmer than average, he tones it down a bit. This way, all the drawings look bala...