The fundamentals of Deep Learning

The fundamentals of Deep Learning

Artificial intelligence (AI) has existed for decades, but it’s come back into the spotlight with big tech companies like Google and Facebook making huge investments in recent years.

The goal of AI is to recreate human cognition and perception with machine learning algorithms as closely as possible. The most advanced form of this technology is called “deep learning,” which attempts to mimic how neurons work inside a brain. You may have seen examples on social media: your favourite photo app automatically tagging people in photos, Netflix recommending TV shows based on what you watched before, or Spotify sorting songs by mood rather than the artist. These are all deep learning models at work – using millions of data points about what someone does online to predict what they might want to do next.

This article will try to cover the fundamentals of Deep Learning and how it is changing the world around us. Let’s get started.

What is Deep Learning?

Deep Learning is a subset of machine learning, which is part of AI. We use machine learning for lots of things: spam filters on email services, voice recognition in smartphones and self-driving cars. Deep learning builds on this by using algorithms called “neural nets” to create computer models that look for patterns in data so they can learn without being programmed explicitly. Artificial intelligence (AI) algorithms used in deep learning are designed to teach machines to detect patterns. This gives computers human-like capabilities that would otherwise be impossible. The capacity to understand speech—and, on the fly, translate it into another language – is just one example of the sophisticated functions that deep learning technologies are capable of accomplishing.

It’s called “deep” learning because it is modelled after the structure of the brain, which contains neurons stacked on top of each other in multiple layers. Each layer is responsible for analyzing different sets of features in the data, just like how a neuron fires only when it receives specific input signals from other neurons on top of it.

How does Deep Learning work?

Deep learning is also called “deep” because of the deep layers of data that are processed. These layers contain various features that are then passed up to the next layer, just like how neurons in our brains pass signals to each other. The primary difference between deep learning and machine learning is that while machine learning examines past data points for predictions, deep learning analyzes all available information at once.

Deep learning systems consist of an input layer, several hidden layers and an output layer. These layers form a pyramid where the data flows from the bottom up to the top, with each layer analyzing different features in order before presenting it to the next one. The input layer takes in the raw data, which is then passed onto each successive hidden layer. You can think of it like a black box where the input goes in one end, and the result comes out the other.

The more layers the system has, the better it is at analyzing data. This allows deep learning algorithms to create complex models that can handle incredibly high-dimensional information like images and videos.

Applications of Deep Learning

The most prominent application of deep learning is in the field of computer vision. The technology can be used to recognize individual faces, objects and even people’s emotions from a video or photo. Deep learning algorithms can also make sense of a scene – for example, determining what a person is doing based on their posture and gestures.

In recent years, deep learning has also been applied to medical, financial, and legal problems. In medicine, the technology is being trained to analyze X-rays to identify abnormalities that may not have been discovered by doctors. In finance, it’s being employed to detect fraud and better manage risk for online transactions. And in law, it’s being utilized to predict how a decision will be made based on previous cases.

The applications of deep learning are endless, and as we progress further and develop more accurate algorithms, the technology will only become more pervasive. How these algorithms affect our daily lives is a question that we can’t answer yet, but it’s sure to change the world in ways never before seen.

The future of Deep Learning

Deep learning is a rapidly advancing field, and the future of deep learning is very bright. With the continued advancement of computer hardware and the availability of large amounts of data, deep learning will only become more accurate and efficient. We can expect to see a more widespread application of deep learning in areas such as healthcare, finance, law and manufacturing.

We can also expect to see more advancements in computer vision, which will create even more opportunities for the technology in robotics and self-driving cars.

Deep learning is already being employed by some of the top tech companies in the world, including Microsoft, Google, Facebook, IBM and Baidu. This will likely continue as more people learn about this exciting and transformative technology.

Picture Credit

Further Reading

IBM Watson Studio – Deep Learning | IBM

Google AI Blog: A Beginner’s Guide to Deep Neural Networks (googleblog.com)

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