ltwm Jay (MATERIAL SCHEDULER)

The most common word is Aata (आटा). This nearly always means whole wheat flour. It's the browner flour you use for making flatbreads like roti and chapati.

But it can be a bit confusing, mate. In India, they have specific words for different types of flour.

The white, all-purpose flour, like what you'd use for cakes, is called Maida (मैदा). It's very different from Aata.

And then there's Besan (बेसन), which is gram flour. That's made from chickpeas. It's used for heaps of snacks.

So yeah, Aata is the main one. But it really depends on the recipe you are using. Hope that helps you out.

"GTR NY" most likely means the Greater New York area. There isn't one single health plan for the entire region. Insurance is typically provided by an employer or purchased through the official NY State of Health marketplace. For a precise answer, you would need to specify the particular company or group.

DRN network is a type of deep learning model. It is used mainly for computer vision tasks. The full form is Dilated Residual Networks. This is a bit of a technical name. But I will try to explain it simply.

First, think about normal neural networks for images. They look for patterns. But they can sometimes lose details. This is because they reduce the image size. DRN tries to fix this problem. It uses something called "dilated convolutions". Imagine you are looking at a picture. Instead of looking at pixels right next to each other, you look at pixels that are spaced out. This lets the network see a wider area. It gets more context without losing the fine details. It is like using a wider lens on a camera.

The "Residual" part is also important. This means it uses skip connections. In a very deep network, information can get lost. Skip connections allow the network to jump over some layers. This helps in training the model better. It prevents the problem of vanishing gradients. This is a common issue in deep learning.

So, why is DRN useful? It is very good for tasks that need precise outlines. For example, semantic segmentation. This is where you label each pixel in an image. Like labeling all pixels that are a car or a road. Because DRN keeps the details, it can do this more accurately. It is also used in image super-resolution. This means making a low-quality image into a high-quality one.

In short, DRN is a smart type of AI model. It is designed to understand images better. It does this by looking at a wider view and keeping important details. This makes it powerful for many applications in computer vision. I hope this explanation helps you understand what a DRN network is.