There has been a lot said and written about machine learning and deep learning lately. Most people understand that these buzz words are somehow connected to artificial intelligence, but they don’t understand specifically how they’re connected. Are machine learning and deep learning the same? If not, what’s the difference? How does it work?
The Difference and How it All Works
How does deep learning work, you may ask. Deep learning is a subset of machine learning. It uses algorithms to create an artificial neural network based on the neural network model of neurons in the human brain. The type of neural network needed to manage the order of magnitude of the number of neurons in the human brain cannot be handled with a simple neural network. That’s why the study of biology has contributed greatly to our computer model and our deep learning model.
What separates the deep learning model from a machine learning model is the concept of unsupervised learning. In a deep neural network, algorithms don’t need to be “trained” by human engineers as they would in a machine learning model. Instead, these algorithms and can be tested, evaluated and selected or terminated by other algorithms.
In order for unsupervised learning to take place, a convolutional neural network of robots needs large amounts of data sets, especially training sets. This is so the robots can train new tasks and get better. Robots that can’t perform new tasks well are eliminated so that better robots can replace them, so that it’s not necessary to start from scratch.
This reinforcement learning for robots has many cycles in its training process, and these cycles are broken down into a number of layers. This number of layers includes, but is not limited to: previous layer, next layer, input layer, output layer and hidden layer.
Deep learning is the closest thing we have to artificial intelligence right now, and the deep learning model is the fastest artificial neural network out there. With deep learning algorithms, the possibilities are endless.
Deep learning applications are myriad. With proper deep learning work, large amounts of data can be aggregated into data sets. This raw data that has been aggregated into a deep learning network can be used for improved feature extraction.
One application of this improved feature extraction is faster and more accurate image recognition. This image recognition can be used to create more sophisticated art prints, fine art prints, illustrators and wall art.
In addition, this improved image recognition through neural network architecture has security and law enforcement applications in its use of the deep network for facial recognition technology and amplified computer vision. Other security and law enforcement applications of the deep learning system include speech recognition. Other speech recognition applications include everything from smart homes to smart cars and the internet of things.
Language translation is another use for a deep learning model. Because of the myriad of possible words and meanings within a natural language, a large amount of data is needed in order to attempt natural language processing, especially if training data to train the algorithms is needed for language translation. Google has been a pioneer in using deep learning models for language translation.
The neural network architectures of a deep learning system can have many applications in many recurrent natural networks. This subset of machine learning has opened up a new field for data scientists, data architects and data analysts, as well as developers and other engineers. These breakthroughs in deep learning have allowed us to adjust our computer model and computer programs in order to make better predictions with many possible adjustments.