Mastering Machine Learning and Deep Learning: Understanding the Differences and Choosing the Right Approach
As a developer, you might be familiar with the concept of Machine Learning (ML) and Deep Learning (DL), but may not be entirely sure about the differences between the two. Both ML and DL are subsets of Artificial Intelligence (AI) and they share some similarities, but there are also some key differences between the two. In this article, I’ll take you through the differences between ML and DL and explore some examples of when to use each approach.
When it comes to Machine Learning, it’s a way of creating systems that can “learn” from data. For example, imagine you want to create a system that can recognize handwritten digits (like the numbers 0–9). You would need to feed the system with lots of examples of handwritten digits so it can learn to recognize them. This is what is called supervised learning, where the system is trained on labeled data to make predictions or decisions. ML also includes unsupervised learning, which is the process of finding patterns or relationships in unlabeled data, and reinforcement learning, which is the process of training a model to make decisions in an environment by receiving rewards or penalties.
Deep Learning, on the other hand, is a subset of Machine Learning that is focused on creating artificial neural…
