What is Deep Learning and How Does it Work?

Many sci-fi writers have predicted that robots would take over the world someday. If by robots, though, you mean algorithms, then today is the day!. However, the artificial intelligence we see in sci-fi is slightly different from the artificial intelligence we see in reality, an artificial neural network. The most advanced artificial neural network to date is the deep neural network. Therefore, it is only normal to ask, “What is deep learning?” Read on for the answer.

1. What is deep learning anyway?

Deep learning is a form of artificial intelligence. It is a subset of machine learning based on an artificial neural network modelled on the biological neural network of the human brain. At the moment, it is the most advanced form of artificial intelligence that we have.

The deep learning model is based on unsupervised learning, which separates deep understanding from classic machine learning (also known as shallow learning).

2. How does deep learning work?

Deep learning algorithms are based on a training process and a learning process. The training data is fed to the algorithms, and they are commanded to perform a new task. This is the training process.

The algorithms are evaluated. Those algorithms that do not succeed at a certain level are eliminated. Those that do grow are used as the model for creating a new “class” of algorithms. This cycle is called a layer, and the cycle is repeated repeatedly until the algorithm has gained a “skill.”. The process of actually gaining skills is the learning process.

The number of layers may vary depending on how deep or how shallow the neural network is intended to be. The types of layers can also vary. An input layer, output layer, first layer, second layer, hidden layer, first hidden layer, previous layer, the final layer, and more are possible input layer options.

These layers are used to form nodes, and these nodes are used to create a deep neural network. The type of neural network can vary, including a simple neural network, traditional neural network, recurrent neural network (RNN) and convolutional neural network (CNN). A convolutional neural network, for example, is used for deep learning applications related to computer vision: image recognition, image extraction, facial recognition and increased pixels.

The breakthrough that separates deep learning from shallow learning is unsupervised learning. Previously, programmers had to administer the entire training and learning process personally. They had to input all of the training data. They had to evaluate all of the algorithms and eliminate underperformers. Then they had to repeat the cycle again and again. That is shallow learning.

Thanks to new advances in artificial intelligence, however, this is no longer necessary with a deep learning model. Now data scientists can create algorithms capable of performing all of the administrative tasks within the training process and the learning process. Algorithms can train other algorithms!

3. Deep Learning Applications

Breakthroughs in recent years have led to a whole host of deep learning applications, Smart homes with features like Amazon’s Alexa, digital assistants like Siri, intelligent security systems like Ring cameras with their facial recognition technology and driverless cars like those made by Tesla.

Google has been a leader in the field of artificial intelligence. Their Google Translate program uses speech recognition technology, made possible by a deep learning algorithm with deep neural networks, to prepare large amounts of data—trillions of elements within datasets—to aggregate all of the possible words and phrases within a natural language for language translation.

Finally, the ability to process big data and large amounts of data has led to the increased development of new sciences within the field of artificial intelligence, such as data analytics, business analytics, predictive analytics and data virtualization. A deeper understanding of data information will continue to lead to more possibilities.