Generative Adversarial Networks (GANs) consist of two main components: a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network aims to distinguish between real and fake data. The two networks are trained simultaneously in an adversarial process, pushing each other to improve their performance. Here is a detailed explanation of the architecture and components of GANs.
This is where fintech companies are successfully leveraging AI. FinTech companies with help of AI are finding cheap, easy and swift methods to apply the technology to an existing business problem at the same time many banks are failing to do so.
Whoever has the best sense for choosing, organizing, uniqueness to combine machine and human skills outlook from the services to collect, clean and label data sets its a market for them that’s just getting started and millions yes billions of dollars are waiting
ML gets the problem-solving call in conjunction with deep learning artificial neural networks. As these jargons i.e AI, ML, DL or ANN etc may be getting their day in the sun, but they’ve been around for a while. It’s just in the past 5-10 years that they have gained traction, technology that was once niche is now becoming more mainstream and cost-effective reaching to common man. Until recent machine learning was known as historical phenomena in the worlds of academia and supercomputing.
How come my bank knows what I am going to buy next, how come my internet browser offering me add on something which I was searching on google few minutes or days backs. How do they know my voice or can recognize my picture without any human intervention. Answer is much simpler then it looks or simpler then the complexity of out own thought process. Use of deep learning