Deep Learning: An educational blog around Deep Learning.


The world of AI is not easy to understand. There are dozens of terms that are thrown

around and it can be hard to know what something means. I'm going to try to help

people who may not have a strong AI background understand what these terms

mean and how they fit together. I am going to cover topics like:

Artificial Neural Networks, Deep Learning, Convolutional Neural Networks, Recurrent

Neural Networks, Natural Language Processing, and, hopefully, Singularity!

This is going to be a big project and you are going to see every wild theory created by

people on the internet which may have the potential to not make sense at all.


Deep learning is a type of machine learning and artificial intelligence (AI) that

mimics how humans acquire specific types of knowledge. Deep learning is a critical

component of data science, which also includes statistics and predictive modeling.

Deep learning is extremely beneficial to data scientists who are tasked with

collecting, analyzing, and interpreting large amounts of data; deep learning speeds

up and simplifies this process.


Consider a toddler whose first word is "water." By pointing to objects and saying the

word "water," the toddler learns what water is and is not. "Yes, that is water," or "No,

that is not water," the parent says. As the toddler continues to point to objects, he

becomes more aware of the characteristics that all dogs share. Without realizing it,

the toddler clarifies a complex abstraction of the concept of water by constructing a

hierarchy in which each level of abstraction is created with knowledge gained from

the previous layer of the hierarchy.


Working on Deep Learning


Computer programs that use deep learning go through much the same process as

the toddler learning to identify water. Each algorithm in the hierarchy applies a

nonlinear transformation to its input and uses what it learns to create a statistical

model as output. Iterations continue until the output has reached an acceptable

level of accuracy. The number of processing layers through which data must pass is

what inspired the label deep.

The learning process in typical machine learning is supervised, and the programmer

must be exceedingly detailed when instructing the computer on what types of


things it should look for to determine whether an image contains or does not

contain a dog. This is a time-consuming procedure known as feature extraction, and

the computer's success rate is totally dependent on the programmer's ability to

precisely define a feature set for. The benefit of deep learning is that the software

builds the feature set without supervision. Unsupervised learning is not only faster,

but it is also more accurate in most cases.


Deep Learning V Machine Learning