Machine Learning vs. Deep Learning: what's the difference and when to use it?
Machine Learning vs. Deep Learning: what's the difference and when to use it?
In an age of evolving technology, the terms "Machine Learning" and "Deep Learning" often appear as part of Artificial Intelligence (AI) technology. These two terms are often considered similar, but actually have fundamental differences in how they work and use. This article will help you understand the difference between Machine Learning (ML) and Deep Learning (DL), as well as when it's best to use both.
What is Machine Learning?
Machine Learning is a subfield of AI that allows computers to learn from data without explicit programming. ML uses algorithms to analyze data, identify patterns, and make predictions or decisions based on that data. Some examples of the application of ML in everyday life are product recommendations on e-commerce platforms, spam email filters, and sentiment analysis on social media.
Machine Learning is divided into several types, namely:
- Supervised Learning: in which an algorithm is trained using a dataset that has been labeled to predict the desired output.
- Unsupervised Learning: algorithms work with unlabeled data to find patterns or relationships within it.
- Reinforcement Learning: where the agent (algorithm) learns through trial and error and is rewarded for each correct action.
- Examples of popular Machine Learning algorithms include K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees.
What is Deep Learning?
Deep Learning is part of more complex Machine Learning, where algorithms use neural networks with many layers, inspired by how the human brain works. This technique is very effective in handling very large and complex data, such as images, audio, and video. DL usually requires more data and high computation to function optimally, so it is usually applied to projects with big data and strong infrastructure.
Deep Learning has a wide range of applications, including:
- Facial recognition and other biometrics,
- Natural language processing such as automated translation and chatbots,
- Computer vision to recognize objects in pictures or videos.
When To Use Machine Learning?
Machine Learning is used when:
Limited Dataset: when the amount of data available is not very large or limited, such as on a small project or limited research.
Simple tasks: for example, to predict numerical values or classify simple data.
Infrastructure limitations: ML can run on devices with lower computing power, making it more suitable for startups or small companies that have limited resources.
Examples of Machine Learning use cases are:
Sentiment analysis in simple text,
Stock price prediction with limited historical data,
Fraud detection in financial transactions with structured datasets.
When To Use Deep Learning?
Deep Learning is best when:
Large and complex datasets: DL requires a lot of data to be properly trained, especially unstructured data such as images or audio.
Tasks that require high accuracy: Deep Learning excels in tasks that are complex and demand high precision, for example in the recognition of faces or objects in images.
Strong infrastructure availability: if it has access to a GPU or TPU, DL can be executed more quickly and effectively.
Examples of Deep Learning use cases are:
Voice recognition in virtual assistants like Siri or Google Assistant,
Automatic object recognition systems in autonomous vehicles,
Medical image analysis to detect abnormalities in X-ray or MRI.
Conclusion
Both Machine Learning and Deep Learning have their advantages and challenges. Machine Learning is ideal for simpler projects with limited data and low computing infrastructure. Deep Learning is more suitable for complex tasks that require high accuracy and work with larger data. Determining the right technology will largely depend on the project's goals, the type of data, and the resources available.
