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Deep Learning

The concept of Machine Learning dates from the middle of the 20th century. In the 1950s, the British mathematician Alan Turing imagined a machine capable of learning; a "Learning Machine". In the following decades, various Machine Learning techniques were developed to create algorithms that could learn and improve independently. We now have both supervised learning and unsupervised learning algorithms, based on the outcome we want to achieve.

Techniques include artificial neural networks. These algorithms are the foundation of Deep Learning, but so are technologies such as image recognition and robotic vision. Artificial neural networks are inspired by the neurons of the human brain. They consist of several artificial interlinked neurons. The higher the number of neurons, the deeper the network is.

Why is deep learning important ?

Deep Learning has many uses. For example, it’s the technology that Facebook uses for facial recognition, with which it automatically identifies your friends on photos. It is also the technology that allows Face ID (facial recognition) on Apple's iPhones to improve over time. Machine learning is also the primary technology powering the evolution of image recognition.
Applications like Skype or Google Translate also rely on machine learning to translate oral conversations in real time. It is also with this technology that the Google Deepmind AlphaGo artificial intelligence has managed to beat the world champion. In recent years, with the advent of convolutional neural networks, Deep Learning is at the heart of computer and robotic vision.


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