Machine learning (ML) and deep learning (DL) are often used interchangeably as identical concepts; however, the two are quite different. While ML forms a part of DL, there are some basic distinctions regarding the specific purposes each aims to fulfill, the operations each was created to execute, and the specific situation that calls for the use of one method instead of the other. The next article intends to clarify the distinction between machine learning and deep learning.
What constitutes machine learning?
Machine learning lies at the core within the larger domain of artificial intelligence and uses statistical techniques that allow programs to "learn" from training sets, thereby removing the need to specifically program them [1]. The aim is to create algorithms that are capable of improving performance on specific tasks through the acquisition of knowledge through learning through experience.
Machine learning can be categorized into three types [2]:
Supervised learning - The training process uses labeled datasets where both the input and corresponding output are provided. It learns to match the inputs to the respective outputs.
Unsupervised learning means that the machine learning method uses unlabeled data to train the machine to recognize the underlying pattern or relationship that exists within the input.
Reinforcement learning involves the system learning through experience within the problem-solving environment, receiving reward or penalty during the process of searching the problem space.
Numerous commonly used machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), and k-means clustering. Such algorithms are used to perform a variety of functions like prediction, classification, and pattern identification.
What is Deep Learning?
Deep learning forms a specialized branch of the larger machine learning domain that uses artificial neural networks to solve complex issues [3]. The method relies upon the nature and operations of the human brain based upon interconnected nodes (neurons) that enable the transfer and processing of information.
In a deep learning system, there are many layers of artificial neurons, each performing a simple computation. The word "deep" in deep learning refers to the depth of the network, that is the number of layers. As information flows through the network, each level learns more abstract features from the output of the next lower level. This methodical method of extracting the features makes the models in deep learning capable of learning complex representations from raw data [4].
Numerous commonly used deep learning structures include:
Convolutional Neural Networks (CNNs) - Mainly used to scan images and videos.
Recurrent Neural Networks are used to analyze sequential information like natural language and textual information.
Long Short-Term Memory Networks (LSTMs) - A type of RNN that can learn long-term dependencies
Generative Adversarial Networks (GANs) - Utilized to generate new datasets, especially visual data like images or videos.
Differences Between Machine Learning and Deep Learning
While both machine learning and deep learning aim to learn from data, there are some important distinctions to be made.
- Data Requirements
Machine learning algorithms typically require well-prepared data along with careful feature engineering done by experienced individuals. However, deep learning has the capability to be used upon raw unstructured data, automatically extracting the required features [5].
- Hardware
Deep learning requires extensive computational power and makes use of complex hardware like GPUs to train complex models within a reasonable time frame. Traditional machine learning algorithms are typically less computationally intense.
- Understandability
A variety of machine learning algorithms such as decision trees are easily understandable and interpretable. However, deep learning models are often viewed as "black boxes" because of the complex structures that make them more difficult to understand [6].
- Performance
For complex problems with large amounts of data, deep learning often outperforms traditional machine learning algorithms. However, for simpler problems or small datasets, machine learning may be more efficient and cost-effective.
When to Use Machine Learning vs. Deep Learning
The choice to use machine learning or deep learning will depend on several factors:
Data availability: With large and complex datasets, the use of deep learning algorithms can be beneficial. However, if the datasets are limited or simpler, the standard machine learning techniques are often sufficient.
Problem Complexity: If the problem involves perceptual tasks like image or speech recognition, deep learning is likely to perform better. For structured data and simpler problems, machine learning algorithms may be more appropriate.
Requirements for Interpretability: Where models need to be interpretable, such as healthcare or finance, machine learning methods like linear models or decision trees are used instead of the use of deep learning.
Computational Power: The use of deep learning requires large amounts of computational power that might make it unaffordable under resource-limited situations. However, machine learning algorithms typically require less.
Conclusion
While machine learning and deep learning are related, they differ in their capabilities, use cases, and underlying architectures. Machine learning encompasses a wide range of algorithms for learning from data, while deep learning focuses on neural networks with many layers. The choice between the two depends on factors like data availability, problem complexity, interpretability needs, and computational resources.
As artificial intelligence continues to evolve, the boundaries between machine learning and deep learning will likely grow more blurred. However, understanding the basic distinctions will help to inform wise decisions regarding solving data-related issues.
References
[1] Mitchell, T. (1997). Machine Learning. McGraw-Hill.
[2] Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
[3] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
[4] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
[5] Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
[6] Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. ITU Journal: ICT Discoveries, Special Issue No. 1, 1-10.
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