In the process of applying AI to business-usecases, one has to consider two different learning algorithms, which perform significantly better in their specific area:
- Machine learning algorithms
- Deep learning algorithms
Let’s dig a bit deeper into this:
Machine learning and deep learning are two subfields of artificial intelligence (AI), with deep learning being a subset of machine learning. While both technologies are based on the concept of enabling machines to learn from data, there are key differences between the two that set them apart.
What’s the level of human intervention needed?
One of the main differences between machine learning and deep learning is the level of human intervention required. Machine learning algorithms require human intervention to a certain extent, as they rely on human-defined rules and algorithms to analyze data and make predictions. In contrast, deep learning algorithms are capable of learning on their own, without the need for human intervention. This makes deep learning algorithms more efficient and effective at handling complex tasks and data sets.
What type of data can they handle better?
Another key difference between the two technologies is the type of data they can handle. Machine learning algorithms are typically used to analyze structured data, such as numbers and text. This means that they are well-suited for tasks such as image and speech recognition, where data is already organized in a specific format. In contrast, deep learning algorithms can handle both structured and unstructured data, such as images, videos, and audio. This makes deep learning algorithms better suited for tasks that require the analysis of complex and unstructured data.
Differences in Performance
In terms of performance, deep learning algorithms are generally more accurate and efficient than machine learning algorithms. This is because deep learning algorithms can learn and adapt to complex data patterns and relationships, while machine learning algorithms rely on human-defined rules and algorithms. As a result, deep learning algorithms are better suited for tasks that require high accuracy and precision, such as image and speech recognition.
Example of a machine leraning algorithm
One example of a machine learning algorithm is a decision tree. Decision trees are a type of algorithm that uses a tree-like structure to make predictions based on a set of rules and conditions. The algorithm starts at the root of the tree and follows a series of rules and conditions to make a prediction. For example, in the task of predicting whether a customer will churn or not, a decision tree algorithm might start by evaluating the customer’s tenure with the company. If the customer has been with the company for a long time, the algorithm might conclude that they are unlikely to churn. If the customer has been with the company for a shorter period of time, the algorithm might evaluate other factors, such as their usage of the company’s services, to make a prediction. This process continues until the algorithm reaches a leaf node, where it makes a final prediction. Decision trees are effective at handling structured data and making accurate predictions, but they require human intervention to define the rules and conditions used in the algorithm.
Example of a deep learning algorithm
One example of a deep learning algorithm is a convolutional neural network (CNN). CNNs are a type of deep learning algorithm that is commonly used for tasks such as image and speech recognition. A CNN works by taking an input image and passing it through multiple layers of filters and transformations. Each layer of filters is designed to identify specific patterns and features in the image, such as edges and shapes. As the image passes through each layer, the algorithm learns and adapts to the data, identifying more complex patterns and relationships in the image. This allows the algorithm to make accurate predictions about the content of the image.
Hope this helps a bit to understand the differences 🙂