Spam Detection in Deep Learning

What is the so-called Spam?

E- messages are a crucial means of communication between many people worldwide. But several people and corporations misuse this facility to distribute unsolicited bulk messages that are commonly called Spam SMS. Spam SMS may include advertisements of medicine, software, adult content, insurance, or other fraudulent advertisements. Various Spam filters are wont to provide a protective mechanism that will design a system to acknowledge spam.

Spam Detection

After submitting your personal details like mobile number or email address on any platform, they started the advertisement of their unusual products by constantly pinging you. They try to advertise by sending constant emails and with the help of your contact details they keep sending you messages as well they are doing WhatsApp more nowadays. Hence, the output is nothing but a lot of spam alerts and notifications popping up in your inbox. This is often where the task of spam detection comes in.

Spam detection means detecting spam messages or emails by understanding text content in order that you’ll only receive notifications regarding your messages or emails that are crucial to you. If spam messages are found, they’re automatically transferred to a spam folder and you’re never notified of such alerts. This helps to enhance the user experience, as many spam alerts can bother many users. Read more…


The Future of Deep Learning Is Photonic

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

While machine learning has been around a long time, deep learning has taken on a life of its own lately. The reason for that has mostly to do with the increasing amounts of computing power that have become widely available—along with the burgeoning quantities of data that can be easily harvested and used to train neural networks.

The amount of computing power at people’s fingertips started growing in leaps and bounds at the turn of the millennium, when graphical processing units (GPUs) began to be harnessed for nongraphical calculations, a trend that has become increasingly pervasive over the past decade. But the computing demands of deep learning have been rising even faster. This dynamic has spurred engineers to develop electronic hardware accelerators specifically targeted to deep learning, Google’s Tensor Processing Unit (TPU) being a prime example.

Here, I will describe a very different approach to this problem—using optical processors to carry out neural-network calculations with photons instead of electrons. To understand how optics can serve here, you need to know a little bit about how computers currently carry out neural-network calculations. So bear with me as I outline what goes on under the hood.

Almost invariably, artificial neurons are constructed using special software running on digital electronic computers of some sort. That software provides a given neuron with multiple inputs and one output. The state of each neuron depends on the weighted sum of its inputs, to which a nonlinear function, called an activation function, is applied. The result, the output of this neuron, then becomes an input for various other neurons. Read more