GAN stands for Generative Adversarial Networks.
In 2014, Jan J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, David Warde, Sherjil Ozair, Bing Xu, Yoshua Bengio, and Aaron Corville published Generative Adversarial Networks paper. Yann LeCun referred to it as the coolest idea in deep learning in the last 20 years.
GANs are generally used to generate images. Converting grayscale images to coloured images, generating realistic human faces, and many other applications are among the most popular.
Before digging deeper into GANs, let’s see the difference between the Discriminative model and the Generative model.
The discriminative model learns to classify data points into…
HTML (Hypertext Markup Language) is the code that defines how a web page and its content are organised. Content may be arranged using a sequence of paragraphs, a list of bulleted points, or photographs and data tables, for example.
Python’s Flask API helps us to create web applications. A Web-Application Framework is a collection of modules and libraries that allow programmers to build applications without having to write low-level code including protocols and thread management.
We’ll look at how to use flask to render a HTML template below.
An activation function plays a vital role in neural networks. It is also called a transfer function. Its aim is to introduce non-linear transformation to learn the complex underlying patterns in the data. It should be differentiable as well as should follow computational inexpensiveness. And its output needs to be zero centered so that it would help in the calculated gradients to be in the same direction and shifting across.
The activation function is represented as f(x) where x=(input*weights)+bias. Now, let’s look into commonly used activation function.
The sigmoid function can be defined as
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