Introduction
When studying Machine Learning or Deep Learning you will come across many different terms such as artificial intelligence, machine learning, neural network, and deep learning. But what do these terms actually mean and how do they relate to each other?
Don't worry I'm here to resolve your all terms and terminology. Have patience and read briefly 😋️
Below we give a brief description of these terms:
Artificial Intelligence
A field of computer science that aims to make computers achieve human-style intelligence. There are many approaches to reaching this goal, including machine learning and deep learning.
Machine Learning: A set of related techniques in which computers are trained to perform a particular task rather than by explicitly programming them.
Neural Network: A construct in Machine Learning inspired by the network of neurons (nerve cells) in the biological brain. Neural networks are a fundamental part of deep learning and will be covered in this course.
Deep Learning: A subfield of machine learning that uses multi-layered neural networks. Often, “machine learning” and “deep learning” are used interchangeably.
Subfields
Machine learning and deep learning also have many subfields, branches, and special techniques. A notable example of this diversity is the separation of Supervised Learning and Unsupervised Learning.
To simplify these terms - In supervised learning, you know what you want to teach the computer, while unsupervised learning is about letting the computer figure out what can be learned.
Application of ML
Machine Learning has had in literary every industry. For example, machine learning algorithms can now detect skin cancer just as accurately as board certificate dermatologists.
- In this case, a deep neural network was trained on hundreds of thousands of skin cancer images and learned to recognize skin cancer from single images.
- Another example has been self-driving cars, thanks to machine learning, we can now develop autonomous vehicles that can drive themselves using only the data from various sensors.
Therefore, learning how to develop deep neural networks means that you are writing software that can potentially change the lives of people all over the world.
I'm sure you're excited to start the journey of Deep Learning as I am.
Let's get started, see you next series of Deep Learning 🤩️