Deep Learning for Dummies

Deep Learning for Dummies: Absolute Basics with No Jittery Terms that Confuse Professionals

What is a cat? And, how is it different from a dog?

Sounds silly to even answer, right! Well, that’s the whole idea of featuring this post today. Most professionals sound inclined to learn technologies without knowing the basic concepts and theorems that drive these the whole time. Why I asked the cat-dog question is because you will need a very sophisticated program to actually enable a machine answer this question correctly. And, it’s possible to teach machines, such as your Computer, TV, Digital banners, mobile phones, smart speakers, cars and so on.

That’s the job and power of deep learning capabilities that allow machines to learn from simple codes that humans write. (In some years to come, even humans won’t write these codes. It would be deep learning machines that will do the coding!)

Now that I have given you an idea what deep learning is for machines, here’s a industry standard definition that you should evaluate in a deep learning training.

What is Deep Learning?

Deep Learning is a highly complex branch of machine learning algorithm structuring that allows machines to process information, learn from them by analyzing data and delivering the information to other machines or humans for further actions. In simpler words, it’s a string of message boxes that add value and insight to the original data, and deliver it to the recipient without adulterating it with noise or misplaced data.

Deep Learning Components

You ought to know Neural Networks.

Like it happens in brains, the thinking CPU of the body and motor control unit, Deep Learning too has neurons. In basic Deep Learning training classes, you will learn the role of Neural Networks and how they form the back bone of this novel science. They are composed to many artificially developed neurons, weights and activation function that are interconnected to each other.

Deep learning design and structuring is based on the source and process of data organization. They are designed based on authority of algorithms, degree of supervision and nature of classification of data. They could be Big Data Deep Learning, Dark Data Deep Learning, and Unstructured Deep Learning.

Based on the technology it is intended to be applied to, Deep Learning components could be classified into Text, Images, Voice and Speech, and Lights.

With better algorithms for machine learning reprocessing, data scientist have managed to develop Hybrid Deep Learning training modules with better optimization for regularization of discriminative and generative architectures.

What Deep Learning can do?                        

A modern deep learning algorithm is expected to tell between the white and black of data. It can be used to tell the difference between text, image, object, voice and other parameters that would be essential to describe the matter or content of data.

Advanced deep learning capabilities are designed to do machine level translations without any pre-processing or recurrent processing. It is used to mainly build machine level language mapping stacked to help machines learn any new language. That’s where Open Source Programming language such as Python and R interject into AI and machine learning projects.

In the end, deep learning can actually tell the various breeds of dog and how they diverge from cat species, without human supervision! Yes, that’s true.

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