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Deep Learning is a subdivision of machine learning that imitates the working of a human brain with the help of artificial neural networks.

Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.

Deep learning systems require large amounts of data to return accurate results; accordingly, information is fed as huge data sets. When processing the data, artificial neural networks are able to classify data with the answers received from a series of binary true or false questions involving highly complex mathematical calculations.

Organizations use deep learning technology in many different ways, some not so expected. For example, deep learning can be used in translations. Although automatic machine translation isn’t new, deep learning is helping enhance automatic translation of text by using stacked networks of neural networks and allowing translations from images.

Deep learning can also be used in text generation. The machines learn the punctuation, grammar and style of a piece of text and can use the model it developed to automatically create entirely new text with the proper spelling, grammar and style of the example text. Everything from Shakespeare to Wikipedia entries have been created.

Currently, there are various neural network architectures optimized for certain types of inputs and tasks. Convolution neural networks are very good at classifying images. Another form of deep learning architecture uses recurrent neural networks to process sequential data.

Both convolution and recurrent neural network models perform what is known as supervised learning, which means they need to be supplied with large amounts of data to learn.

In the future, more sophisticated types of AI will use unsupervised learning. A significant amount of research is being devoted to unsupervised and semi-supervised learning technology.

Want to learn more? Fundamentals of Deep Learning is a 2-day course that brings a hands-on approach to understanding the deep learning methods, algorithms, and computational tools used in modern engineering problem-solving.

This innovative program connects your deep learning, science and engineering skills to the principles of deep learning, machine learning and data science. With an emphasis on the application of deep learning methods, you will put these new skills into practice in real time.

For more information, questions, comments, contact us.

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