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Deep Learning Tutorial

Deep structured learning or hierarchical learning or deep learning in short is part of the family of machine learning methods which are themselves a subset of the broader field of Artificial Intelligence.

Deep learning is a class of machine learning algorithms that use several layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.

Deep neural networks, deep belief networks and recurrent neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have.


Deep learning applications

Self-Driving Cars : In self-driven cars, it is able to capture the images around it by processing a huge amount of data, and then it will decide which actions should be incorporated to take a left or right or should it stop. So, accordingly, it will decide what actions it should take, which will further reduce the accidents that happen every year.

Voice Controlled Assistance : When we talk about voice control assistance, then Siri is the one thing that comes into our mind. So, you can tell Siri whatever you want it to do it for you, and it will search it for you and display it for you.

Automatic Image Caption Generation : Whatever image that you upload, the algorithm will work in such a way that it will generate caption accordingly. If you say blue colored eye, it will display a blue-colored eye with a caption at the bottom of the image.

Automatic Machine Translation : With the help of automatic machine translation, we are able to convert one language into another with the help of deep learning.

Limitations

  • It only learns through the observations.
  • It comprises of biases issues.

Advantages

  • It lessens the need for feature engineering.
  • It eradicates all those costs that are needless.
  • It easily identifies difficult defects.
  • It results in the best-in-class performance on problems.

Disadvantages

  • It requires an ample amount of data.
  • It is quite expensive to train.
  • It does not have strong theoretical groundwork.

TEXT BOOKS:

  1. Deep Learning By Ian Goodfellow and Yoshua Bengio and Aaron Courville


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