Deep Learning is Revolutionizing the Field of Study

  Technology: An Update on Deep Learning

Deep learning is a subset of machine learning (Machie Learning) that falls under the field of artificial intelligence (AI). This technology involves teaching a computer model to learn.

It's a bit like, but you have to be wary of comparisons, when a child learns from his parents and his teachers.

In very simple terms, a computer model is shown different images of different objects and told what each represents. With training, the model can learn to recognize and categorize different images on its own. And also to recognize and learn from new images.

Deep learning is, for example, essential to the operation of self-driving cars. A driverless car uses a combination of cameras and sensors to capture data from its surroundings, such as traffic lights, pedestrians and other cars on the road. It then processes this data to determine the best course of action: slow down, stop, roll, etc.

 How does deep learning work?

The capabilities of deep learning differ in several ways from those of traditional machine learning. Deep learning can help computers solve many complex problems.

This technology uses neural networks. Here too we can make a comparison with the activity of the human brain. Just as the brain contains layers of interconnected neurons, so does an AI neural network, with nodes interconnected to share information.

Training these deep learning neural networks can be time-careful because it requires the ingestion of large amounts of data. And many iterations for the system to gradually refine its model to obtain the best result.

Neural networks are developed into sprawling networks with a large number of layers that are trained using massive amounts of data. These deep learning neural networks are behind the current leap forwards in the ability of computers to perform speech recognition, numerous generative AI capabilities and also advances in the field of health.

What are some examples of deep learning?

Today, deep learning be everywhere, in corporate AI services and in the voice assistant on your smartphone!!!

Here be some of the most popular deep learning applications:

 ChatGPT

•     penAI's chatbot uses deep learning and be one of the largest deep learning models available. ChatGPT uses version 3.5 of a pre-trained generative transformer (GPT 3.5) , which displays 175 billion parameters. The neural network that makes ChatGPT be so effective be trained to learn patterns and relationships in language.

•  Virtual assistants

•      Voice assistants, such as Google Assistant , Amazon Alexa , and Apple's Siri , use deep learning for speech recognition and natural language processing. They apply these deep learning techniques to process what you tell them and respond appropriately and accurately.

• Fraud detection

•     Various entities can use deep learning to detect and prevent fraud. Financial institutions, for example, use different algorithms to detect fraud. One example you may be familiar with be Long short-term memory (LSTM) , a deep learning model that flags suspicious activity that deviates from the data it was trained on.

• Health

•    Artificial intelligence has already had a significant impact on the healthcare sector. Deep learning technology has been found useful in diagnosing eye diseases, including diabetic retinopathy and glaucoma, and even some cancers.

What be the differences between machine learning and deep learning?

Artificial intelligence encompasses many areas of research that can make machines capable of performing tasks that would normally require human intelligence. And this ranges from genetic algorithms to natural language processing.

Machine learning be a subset of AI and be defined as the process of teaching a computer to perform a task rather than programming it to perform that task step by step.

Deep learning, on the other hand, be a subset of machine learning, whose capabilities differ in several key ways from traditional machine learning, allowing computers to solve a multitude of complex problems that could not be approached differently. Here be two examples to better understand.

•  Machine learning can make predictions from data. For example, determining whether a fruit in a photo be an apple or an orange.
• Deep learning can solve more complex problems, such as handwritten digit recognition , where a massive amount of data be required during training.