A Not-So-Deep Introduction to Deep Learning

Deep learning introduction simplified

Photo by Pietro Jeng on Unsplash

Some background first

Artificial intelligence is the field of computer science which focuses upon building algorithms that enable machines to mimic the working of the human brain to process data and information so that they become able to provide informed future decisions. Artificial intelligence is a vague field which has various branches like natural language processing, robotics, expert systems etc. Machine learning is one of such branches of AI that uses computer science and statistics to enable machines to learn from data and past experience to solve a problem by identifying patterns and making predictions or giving decisions without the need to be explicitly programmed.

Machine learning algorithms require human intervention to be taught what features should be extracted from the data and what the desired output is. However, there is a sub-field of machine learning, called deep learning, that can automatically extract the useful features from the data to be able to make predictions or give decisions. While machine learning algorithms require more structured data to learn, deep learning algorithms are powerful enough to process raw unstructured data and to determine the set of features that differentiate the data under various classes.

TensorFlow and PyTorch are some popular tools used for deep learning.

Why Deep Learning?

Traditional machine learning algorithms require more structure data and human intervention to determine the set of features that enable them to figure out the difference between the input data. For instance, if face detection is to be performed using traditional machine learning algorithms, the approach that they take is identifying the various parts of the face like eyes, ears, mouth, nose etc. first. To be able to identify those parts, the algorithm has to decompose each of those parts and study their low-level features like the orientation and direction of their lines or pairs of lines etc., which is complex and time consuming when performed manually. This means that the human expert has to teach the machine algorithm about the different features that may be present in a face.