Concept, Definition, and Examples
Deep Learning concept is one of the leading technologies for Machine Learning and Artificial Intelligence. Find out what this technology is, how it works, and its different application areas.
The simple definition of deep learning and its origin
The concept of Machine Learning dates back to the mid-20th period. In the 1950s, the British mathematician envisioned a machine capable of learning, a “learning machine.” During the following decades, different machine learning techniques were well-known to create algorithms that could learn and improve independently.
Deep learning versus machine learning? Not really
Among these techniques are artificial neural networks—Deep Learning on these algorithms and technologies such as image recognition or robotic vision. Neurons inspire artificial neural networks in the human brain. They consist of several artificial neurons linked together. The greater the number of neurons, the deeper the network.
How does it work?
Each neuron receives approximately 100,000 electrical signals from other neurons in the human brain. Each active neuron can produce an excitatory or inhibitory effect on those it connects. In an artificial network, the principle is comparable. Signals travel between neurons. However, the neural network assigns a certain weight to the different neurons instead of an electrical signal. The last layer of neurons emits a response to these signals.
Let’s take a material example of image recognition to recognize how deep learning works. Imagine that the neural web used to acknowledge photos that include at least one cat. The algorithm must distinguish different types of cats and accurately identify a cat regardless of the angle from which it is a photograph.
Deep Learning Examples
Deep learning has many uses. This technology is used for Facebook’s facial recognition, for example, to identify your friends in photos automatically. This technology also allows Apple’s iPhone X facial recognition to improve. As described above, machine learning is also the core technology for image recognition.
To translate conversations in real-time, software such as Skype or Google Translate also depends on machine learning. Cheers to this technology, the artificial intelligence Google Deepmind AlphaGo succeeded in prevailing over the world champion. With the emergence of convolutional neural networks in recent years, deep learning has been at the heart of machine vision and robotic vision.
As Professor Peter Corke explains, since artificial neural networks mimic the functioning of the human brain, the opportunities offered by this technology will increase as we discover the secrets of our organs. By understanding the algorithm behind the human brain and the ways evolution has given us over time to understand images, reverse engineering will bring the potential of the human brain to artificial networks.
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