Can machines learn and think just like humans? In short, the answer is… kind of. Similar to humans, machines are now able to utilize data fed to them to generate output or make decisions. If you take away the emotional aspect of humans, machines can act in a similar manner, and maybe even make better decisions.
Now, you might always hear the terms machine learning or deep learning, but what is the difference between them or are there even any? Both machine and deep learning have the same goal in mind which is to extract insights, patterns and relationships that can be used to make decisions. However, deep learning is a specialized form of machine learning.
What is Machine Learning?
Imagine you are back in elementary school and learning your multiplication table for the first time. Given a multiplication table, you will be able to answer any simple multiplication question your teacher asks you by referring to the numbers within the table. If your teacher asks what is 2×5, the correct answer of 10 will be on the table, however if you answer incorrectly, the teacher will step in and correct your mistakes. Similarly, a set of inputs are received in machine learning along with a set of correct outputs. Learning is done by comparing actual output with correct output and finding errors to correct for future references. An algorithm is trained by giving large quantities of data to the algorithm and allowing it to learn more about the processed information. Machine learning is supervised by a data scientist who jumps in with corrections if a problem occurs to make correct changes. In short, machine learning uses already existing data to create favourable outputs.
Machine learning is widely used all over. Many banks use machine learning to anticipate fraudulent acts. The system takes the transaction details such as the amount, merchants, location and time to determine whether the client is a fraud. Additionally, Amazon and Netflix integrated machine learning to create a unique experience for users. The suggested movies or products that pop up right when you open Netflix or Amazon is determined through data of your previous views and correlated to people with similar views.
What is Deep Learning?
Deep learning is a specialized subset of machine learning. An artificial neural network (aka deep learning) alludes to the fact that the system is created with a similar structure to the neurons in our brains. Just like how we use our brains to identify patterns and classify various types of information, the system learns, identifies and makes intelligent decisions on its own. Without the assistance of a human, the machine is taught to learn by example. Collecting large amounts of data, deep learning searches for complicated patterns and make connections between those patterns in order to make a decision or determine if an output is correct or not.
The science of deep learning has many applications such as self-driving cars and speech recognition. For a car to be autonomous, deep learning is applied to a system which recognizes people, lamp posts, stop signs, etc. Building a connection between the objects around the car and previous actions that were done, cars are able to determine the appropriate actions to take on the road. For example, if the car senses a car next to it, it will wait to merge into the other lane. On the other hand, speech recognition is also possible because of deep learning. Siri, Alexa, and Google are able to recognize our voices because of the large amounts of data and the connections they make. Hundreds of people’s voices of many different languages are compiled and the system splits every sentence up to determine connections between them to ultimately recognize what we are saying to it. Just like how a baby is able to learn and recognize its parent’s voice, speech recognition picks up on the sound of people’s voices and correlates it with the demands we ask of it.
Let’s break this down
Here are a few of the major differences between machine learning and deep learning:
- Data Volume: Machine learning only requires thousands of data points whereas deep learning requires millions
- Output: Deep learning does not only have numerical values as an output, like machine learning, can basically produce any output necessary such as free-form elements.
- Discovering Features: Machine learning requires features to be used for classification provided manually, where as deep learning discovers them automatically.
- Human Interaction: Deep learning is more a resemblance of human learning as it does not need a human to intercede in the process.