What is the Difference Between Machine Learning and Deep Learning?

Written: May 24, 2019 UNPAID

What is the Difference Between Machine Learning and Deep Learning?

Recently, top computing companies have confirmed that today’s generation has surpassed the time of the mobile-first world. Presently, everything that we do now are in the artificial intelligence world. We now have digital assistants and services that provides information and helps people get the job done. Smartphones and personal computers are just secondary tools utilizing artificial intelligence.

This new development in technology beings two terms that warrant serious consideration for tech geeks and the like. One would often hear the terms - machine learning and deep learning. These methods are being used to “teach” artificial intelligence to perform specific tasks. But these methods of learning go beyond the creation of “smart” assistants. So what is the difference between machine learning and deep learning? Here are a few facts.

What is Machine Learning?

Using machine learning, computers today can now see, speak and hear. Computers are now made “trainable” to predict the weather, understand shopping preferences, predict the stock market outcomes, control robots in a factory and others. Many popular consumer-related services such as Google, Netflix, Facebook, and LinkedIn are all backed up by learning machines. And at the heart of all these learning is what we call - algorithm.

An algorithm is not a complete computer program nor a set of instruction. Algorithms have a limited sequence of steps that connects to solve a problem. For instance, a search engine will rely on an algorithm that seeks the texts entered into the search field, and search the connected database to come up with related search results. It will take several steps to achieve this specific goal.

Machine learning started in 1956 during the time of Arthur Samuel. He did not like to write a highly-detailed or lengthy program that will enable a computer to beat him in a Checkers game. So he created an algorithm that allowed the computer to play against itself thousands of times until it “learn” how to play as a stand-alone opponent. Several years later, in 1962, the computer was able to beat the Connecticut State champion on a game of checkers.

What’s the core of Machine learning?

Based on its development from the start, machine learning basically is all about trial and error. There is no way to manually write a program by hand which will help a self-driving car learn to differentiate a pedestrian from another car or tree. But developers create algorithms for this program that can solve this problem using data. Presently, there are algorithms created to help programs in determining the path of a hurricane, find out the world’s highest-paid basketball stars and diagnose chronic diseases early on.

Low-end devices run machine learning algorithms and break down problems into smaller parts. These parts are solved in a specific order and they combined together to come up with a single answer to a problem. Through machine learning, a computer program is “learning” from experience based on the improvements in their performance of specific tasks.

The core of machine learning algorithms essentially enables programs to create predictions and over time, they get better with their predictions based on experience as well as on trial and error.

Four Types of Machine Learning

There are basically four main types of machine learning that is currently being utilized to help computer programs. They are:

Supervised Machine Learning

In this type of machine learning, a computer program is provided with labeled data. For example, an assigned task is to segregate photos of boys from girls using an algorithm that sorts images. Then images of a male child will be tagged as “boy” and images of a female child will be labeled as “girl.” This is what they call the “training” dataset. The labels will remain in place until such time that a program can successfully sort the images at an acceptable rating.

Semi-Supervise

In this type of machine learning, the computer program will be provided with a few labeled images and will utilize the algorithm to make guesses on the unlabeled images. The data is sent back to the program as training data. Then a new batch of images will be provided again with only a few having labels. It is a repetitive process that the program will undergo until it can successfully determine boys and girls at an acceptable rating.

Unsupervised Learning

Using the same example of distinguishing images of boy and girls, in unsupervised machine learning, the program will be thrown into the task of splitting images of boys and girls and put them in two groups with one of two methods. An algorithm will ensure “clustering” and it will group similar object together based on certain characteristics like eye placement, hair length, and jaw size. Another algorithm will be used to “associate” the rules created by the program based on the similarities discovered in the task. This learning type determines a common pattern between the images and then sorts them together accordingly.

Reinforcement Learning

A program will be provided with all the rules of the game and the instruction on how to play. It will go through many steps to complete a round. A game of chess is a great example of the algorithm using reinforcement machine learning. The program will only be provided with the information of whether it won or lost the game. It will continue to replay the game and keep track of all the successful moves until it finally wins.

What is Deep Learning?

When talking about deep “machine” learning, it is basically taking learning on a deeper level. It is inspired by how the human brain functions but to make this possible it will need a high-end machine with discrete graphics cards capable of exploring numbers and huge amounts of data. Smaller amounts of data actually yield lower performance.

Compared with machine learning algorithms that break down problems into smaller parts and individually solves them, deep learning algorithms solve the problem from end to end. The reason why it needs a more “high-end” machine is that the more data and time fed to a deep learning algorithm, the better it becomes at solving a task.

For instance, in the task of separating images of boy and girls, the algorithm for machine learning relied on data that is readily provided. Deep learning algorithm will not be provided with any data for the program to base its analysis. It will scan pixels within an image to learn the edges that can be used to determine a boy or a girl. After which the algorithm will place edges and shapes into an order of possible importance in determining the two genders.

Putting it simply, deep learning does not require information provided by humans to come up with an answer. It will use an algorithm to determine and figure out the answer to a problem that exists. Deep learning uses a “deep thinking” process that needs bigger hardware to process the huge amount of data that the algorithm generates. Usually, deep learning machines are located in large data-centers to create that artificial neural network capable of handling big data generation and supplies this to an artificial intelligence application. Most of the time, programs using deep learning algorithms take some time to train. This is because they learn on their own instead of using human-fed information and shortcuts.

In deep learning, the task is broken down in ways that allow almost all kind of machine-assistance possible. With the help of deep learning and artificial intelligence, impressive applications are now being utilized and deployed some of these are -

Recoloring Black and White Images

Computer programs are now taught to recognize objects and learn how they should appear to human eyes. With this, colors can be added on black and white images or videos.

Precision Medicine

Deep learning algorithms have learned techniques to develop medications genetically tailored for an individual’s genome.

Automated Reporting and Analysis

New systems employing deep learning algorithm can now analyze data and provide report insights based on its natural environment, human language and even provide infographics which can be easily understood by humans.

Games

Far from the time that machine learning was first developed in the 1950s, deep learning systems are also used to develop more challenging games and they are now available in various mobile and video games.

Language Translation

New technology is being used to translate the words of presenter into different languages through text and electronic voice and in real time. It took many years to achieve this feat because of the differences in language use, voice pitch and the maturing capabilities of the hardware.

ChatBots and Social Media

Deep learning algorithms are also present in most conversational chatbots for Amazon, the Alexa, Cortana, and Facebook. They provide contact and page suggestions and even assists companies creep their advertising into social media feeds.

Machine learning and deep learning has shaped the way our technology works today. Deep learning is basically a subset of machine learning which requires a more sophisticated set of hardware and system to accommodate a large amount of data in all its processes. Developers are looking to the future as they create “smarter” devices and appliance that are trainable through a machine or deep learning. Deep learning has become more refined and we can expect more of its innovations and applications. Over time, computers will become an intelligent assistant that will help humans through their day.