Machine learning vs Deep Learning

Software Development | Business, Design, Technology | Jun 3,2021 | By Ajay Kumar

Machine learning and deep machine learning are frequently used interchangeably in the AI sector. That, however, is not the proper course of action!

Machine Learning is a subset of artificial intelligence and Deep Learning, on the other hand, is a subset of machine learning. So the two notions related in several ways are not interchangeable.

How is Machine Learning different from Deep Learning?

Feature Engineering, an essential factor:

Feature engineering is a method of constructing feature extractors. These feature extractors help to learn algorithms operate better by making data less complicated and patterns more apparent. However, this procedure demands patience and competence.

In machine learning, an expert must first identify the majority of the features used. The data and domain type are then hand-coded into these features. The accuracy of the recognized and extracted features determine the performance of a machine learning algorithm

Learning speed

Because there are fewer factors, machine learning algorithms are quick to train. Deep learning, on the other hand, is the polar opposite. Machine learning also outperforms deep learning in terms of prediction speed.

Hardware dependency

You don't need much to train machine learning models. For example, one or more CPUs should be sufficient to exercise a classical model. Deep learning models, on the other hand, require hardware accelerators such as TPUs or GPUs. Deep learning models might take months to prepare if these are not the accelerators used.

Problem-solving techniques

When using a machine learning method to tackle an issue, you must first divide it into sections. These aspects of the problem are tackled independently. To arrive at the outcome, the answers to these difficulties are merged. Problem resolution, on the other hand, is an end-to-end process in deep learning.

Reliance on data

When the amount of data is small, deep learning algorithms perform poorly. What is the reason behind this? Because they require a large amount of data to comprehend and recognize patterns. In this case, traditional machine learning techniques do significantly better.

Interpretability/reasoning behind the results

Machine learning has higher readability than deep learning. When you apply a deep learning algorithm to tackle an issue, you're likely to receive near-human results. But it's hard to figure out why it came up with that outcome. After all, it was a complicated neural network at action, and it's challenging to know what the neurons were doing collectively to arrive at that outcome. The standard machine learning problem-solving technique, on the other hand, is not the same. In most cases, algorithms such as decision trees and logistic regression are used, led by specific limits.

Applications based on machine learning and deep learning

Speech recognition, natural language processing, and picture classification are the most popular applications using Deep Learning. Machine learning is utilized in medical diagnosis, product suggestion, social media features, statistical arbitrage, and other classifications and forecasts.

Machine learning and deep learning have enabled machines to function nearly, making human life easy.

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