What is Deep Learning? What are its applications?

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.

Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.
Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

Deep Learning vs. Machine Learning






How is Deep Learning Used: Applications

Deep Learning applications have started to surface but have a much greater scope for the future. Listed here are some of the deep learning applications that will rule the future.

  • Adding image and video elements – Deep learning algorithms are being developed to add colour to the black and white images. Also, automatically adding sounds to movies and video clips.

  • Automatic Machine Translations – Automatically translating text into other languages or translating images to text. Though automatic machine translations have been around for some time, deep learning is achieving top results.

  • Object Classification and Detection – This technology helps in applications like face detection for attendance systems in schools, or spotting criminals through surveillance cameras. Object classification and detection are achieved by using very large convolutional neural networks and have use-cases in many industries.

  • Automatic Text Generation – A large corpus of text is learnt by the machine learning algorithm and this text is used to write new text. The model is highly productive in generating meaningful text and can even map the tonality of the corpus in the output text.

  • Self-Driving cars – A lot has been said and heard about self-driving cars and is probably the most popular application of deep learning. Here the model needs to learn from a large set of data to understand all key parts of driving, hence deep learning algorithms are used to improve performance as more and more input data is fed.

  • Applications in Healthcare – Deep Learning shows promising results in detecting chronic illnesses such as breast cancer and skin cancer. It also has a great scope in mobile and monitoring apps, and prediction and personalised medicine.

If you are interested in the domain of AI and deep learning and want to learn more about the subject, check out Great Learning’s PG program in Artificial Intelligence and Machine Learning.

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