Deep Learning

Deep learning is a rapidly changing field at the intersection of computer science and mathematics. The purpose of the machine learning is to teach the computer to do various tasks based on the data. It is essential to know the language of mathematics and programming for deep learning.

Deep learning have become an important area of artificial intelligence because of their success in many different fields.With deep learning, computers have the ability to interpret large amounts of data without explicit instructions (such as if else).

There are many similar definitions on deep learning. For more detailed descriptions, you can review the comments on the link below.

What is deep learning? Why is this a growing trend in machine learning? Why not use SVMs?

The following picture summarizes the historical development of deep learning. It is exciting that computer learning, which is alleged to be unable to solve even the XOR problem, reaches these levels.

Source:http://beamandrew.github.io/deeplearning/2017/02/23/deep_learning_101_part1.html

Instead of identifying a computer as a computer, for example, connecting to the Internet and feeding it with millions of tablespaces, the computer will now have the ability to recognize a new table, which can now be faced. This process will result in a computer that knows when a tally is visible. Having the ability to reach the capacity of the table objects among the millions of objects after the learning is provided within the next few seconds both frightens and excites the people.

Let’s see what computers can do with deep learning.

He can beat the world’s best Go players.

He can detect and identify objects.

Source:https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html

He can interpret the pictures.

“black cat is sitting on top of suitcase.” — -> Kaynak:http://cs.stanford.edu/people/karpathy/deepimagesent/

Deep learning is not limited to the above topics. The link below has a nice compilation.

Deep Learning is Revolutionary

We can rank Tensorflow, Theano, Caffe, Pytorch, CNTK, MXNet, Torch, Deeplearning4J, Caffe2 as popular deep learning platforms. The classification of some platforms according to certain characteristics is as follows.

source:http://www.datasciencecentral.com/profiles/blogs/open-source-deep-learning-frameworks-and-visual-analytics

Deep learning related online courses are compiled on the link below. (paid or free)

Dive into Deep Learning with 15 free online courses

In this article, we started by touching on topics such as deep learning, deep learning tools, and what can be done with deep learning. You can look at the link below to remember the general headings while the indentation method continues.

Big Data, Artificial Intelligent and Cyber Security