What’s the Difference? Machine Learning vs Deep Learning

Artificial intelligence (AI) has significantly transformed various industries, and machine learning and deep learning are two essential domains in this field. Both of these technologies have gotten a lot of attention in the past few years because of their unique capabilities in problem-solving. Although both terms sound similar and often used interchangeably, there are crucial differences between them. In this article, we’ll take a closer look at what Machine Learning and Deep Learning are and the differences between them.

What is Machine Learning?

Machine Learning is a subfield of AI that enables software applications to predict outcomes accurately without being explicitly programmed. This technique uses statistical algorithms to learn from data, then makes predictions accordingly. In simple terms, machine learning focuses on constructing predictive models using data samples with input features and output variables. The program will learn to identify patterns within the dataset and form complex correlations enabling the software to predict outcomes with confidence. For example, online retailers like Amazon use machine learning to suggest similar items a customer might be interested in purchasing based on previous purchases.

What is Deep Learning?

Deep Learning is a subset of Machine Learning that utilizes artificial neural networks to learn from large amounts of data. Essentially, it mimics the way the human brain works. Deep Learning models are organized in layers, with each layer extracting specific features from the input data. In other words, Deep Learning algorithms improve their performance by learning from the previous layer’s extracted features, making it possible to perform more complex tasks such as image and speech recognition.

Key Differences

The primary difference between Machine Learning and Deep Learning is how they approach problem-solving. Machine Learning algorithms will learn from previous examples and apply that learning to processes such as identifying cars from pictures of cars. Deep Learning would take this concept a step further by recognizing the different parts of the car—doors, windows, wheels—which can better help identify the car. In other words, Machine Learning is about pattern recognition, while Deep Learning goes one step further and forms an understanding of that pattern to solve more difficult problems.

The other significant difference is the amount of data required for both technologies to function. Machine Learning requires significant processing power and vast amounts of structured data to perform effectively. Meanwhile, Deep Learning requires more data. The more extensive the data, the better the results, and performance increase.

Examples of Machine Learning and Deep Learning in Action

A great example of Machine Learning in action is the use of recommendation engines like Netflix or Amazon. On the other hand, Tesla’s self-driving cars are an excellent example of Deep Learning in action. The cars built with complex Deep Learning algorithms are capable of identifying objects such as pedestrians, traffic lights, and other cars while driving.

Conclusion

Machine Learning and Deep Learning, both related to Artificial Intelligence Arena, are two technologies that are making their way into our daily lives. Understanding these differences between them is crucial to determine which technology is best for a business application. Remember, Machine Learning is primarily concerned with pattern recognition, whereas Deep Learning forms a better understanding of patterns to solve more complicated problems. Both of these technologies are still evolving, and there’s much more to discover and explore in the future.

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