Is Machine Learning the same as Deep Learning?

Machine Learning (ML) is a branch of AI that focuses on the usage of data and algorithms. Data is distributed into the algorithm to make the prediction. In simple terms, we want to make computers learn from the data to make the decision rather than tell them what to do. Some of the use cases of ML are the identification of spam emails, sentiment analysis and house price prediction.

In simple terms, Deep Learning (DL) is part of machine learning (ML) that replicates the way humans gain experience. DL growth commenced in 2006 after Geoffery Hinton published Deep Belief Networks and layer-wise pretraining technique (2006, Hinton) paper. Since then, DL began to be a buzzword around the world and in different industries. In 2022, we have numerous articles published on DL and ML and we have come a long way. There have been countless use cases in diverse industries from image recognition to recommendation engines. Are we getting close to Singularity? The term Singularity signifies a hypothetical forthcoming point in time where Artificial Intelligence becomes self-aware, out of control and more intelligent than human beings. I will leave this topic for next time.

It is essential to understand the differences between DL and ML. I often see people using ML and DL interchangeably which makes sense in some cases. As we already know, deep learning is machine learning, or we could also say the evolution of machine learning over time. Machine learning enables computers to perform tasks without explicitly programming whereas Deep learning is purely based on neural networks where a machine trains itself to perform tasks. In my opinion, machine learning is widely used compared to Deep learning because DL models are extremely difficult to explain. There are certain industries such as finance, healthcare and retail where it is very important to explain the prediction made by the models.

There are several differences between ML and DL and similarities as well. The uses of both ML and DL algorithms are growing exponentially. There have been several breakthroughs in these fields of study. The future of ML is expected to grow, and businesses are adapting ML applications as their primary solution to business problems. Most industries are already leveraging the power of data and ML algorithms to grow. Overall, it looks exceptionally exciting, thanks to all the research and development that is happening in the field of AI.

References:

Geoffrey Hinton, Simon Osindero, and Yee-Whye Teh, “A fast learning algorithm for deep belief nets”, Neural computation 18.7, 2006