People often confuse the terms deep learning and machine learning, but this isn’t the case at all. Therefore, anyone wishing to gain a deeper understanding of artificial intelligence should understand its terms and their differences. Good news: it’s not challenging at all. Deep learning is, in essence, the process of machine learning.
Let’s understand this concept in depth-
What is Machine Learning?
The general term for when computers are taught through data is machine learning. An algorithmic approach to making predictions, based upon patterns recognized when looking at data, is the intersection of computer science and statistics. Depending on the type of data being fed into the algorithms, these algorithms can learn either supervised or unsupervised. Ultimately, machine learning is just complex math applied to coding that serves the same mechanical function as a flashlight, a car, or a computer screen.
Now let’s examine what is deep learning is all about!
What is Deep Learning?
As well as being sophisticated and mathematically complex, deep learning algorithms are also an evolution of machine learning algorithms. Deep learning algorithms have been in the news lately and for good reason. Recent technological advances have led to results that were previously unthinkable. Deep learning can result from both supervised and unsupervised learning. It involves algorithms that analyze data according to a logic structure similar to a human’s reasoning. For deep learning, artificial neural networks (ANNs) are used as layered algorithms. These artificial neural networks are derived from the biological neural networks of the human brain, leading to a learning process that is far more capable than standard machine learning models.
Today, deep learning is used in many different fields, including automated driving. In this context, deep learning identifies objects like STOP signs and pedestrians through a computer vision algorithm. Deep learning is used in the military to detect threats from satellites, e.g., to determine safe or unsafe zones for its troops. Of course, deep learning is used in consumer electronics as well. The Amazon Alexa device, for example, responds to your voice and learns your preferences through deep learning algorithms.
The Difference between Machine Learning vs Deep Learning!
Machine learning is deep learning in practical terms. It functions in a similar way to machine learning (hence why the terms are sometimes mixed up). However, its abilities are different. In general, machine learning models get better at what they are doing with time, but they still need guidance. If an AI algorithm returns an incorrect result, an engineer will need to take over and correct it. An algorithm using a deep learning model can decide through its own neural network whether or not a prediction is accurate.
- With machine learning, algorithms parse data, learn from the data, and then create an artificial neural network that analyses and makes decisions based on that learning. With deep learning, algorithms are structured in layers that create an artificial neural network that learns and makes decisions on its own.
- Mechanics and deep learning are both subfields of artificial intelligence. However, deep learning is what enables the most human-like artificial intelligence.
- A machine learns by thinking and reacting without much human intervention; deep learning is when computers learn to think by using brain-inspired structures.
- A deep learning algorithm requires less computing power and can analyze images, videos, and unstructured data, which machine learning cannot. Deep learning can analyze images, videos, and unstructured data far more efficiently than machine learning.
- There will be career paths involving machine learning and deep learning in every industry.
- Deep learning uses neural networks and can deal with large volumes of unstructured data, while machine learning relies on structured data and linear regression.
- The technology behind deep learning is paving the way for more sophisticated and autonomous programs, including self-driving cars and robots that perform advanced surgery. You already see machine learning in your email inbox, bank, and doctor’s office. You already see machine learning in your email inbox, bank, and doctor’s office.
- Compared to machine learning systems that can be easily set up and operated, deep learning systems can have instantaneous results while taking longer to set up and operate. (Although the quality may improve as more data become available over time).
- Deep learning systems are more complex than machine learning programs and require more powerful hardware and resources. Deep learning algorithms are largely more complicated, but machine learning algorithms are generally simpler and often compatible with conventional computers.