The Advantages of Machine Learning over Deep Learning
The field of Artificial Intelligence (AI) is constantly evolving, and offers a variety of approaches to solving complex problems. Two methods are widely used. These include Machine Learning and Deep Learning. Although Deep Learning is often seen as a powerful extension of Machine Learning, there are cases where more traditional Machine Learning techniques offer distinct advantages over Deep Learning.
Discover the advantages of Machine Learning and how it can outperform Deep Learning in terms of efficiency, interpretability and resource requirements.
1. Model interpretability
Machine Learning models such as decision trees (Random Forest) are easier to interpret and explain than Deep Learning neural networks. Indeed, this can be crucial in areas where transparency and model understanding are important, such as the sciences.
2. Less computing power needed
Machine Learning models are less complex than Deep Learning neural networks. This means they require significantly less computing power for training and application. As a result, Machine Learning is more accessible in environments where computing resources are limited.
3. Less data required for Machine Learning:
Machine Learning models can be effective with much smaller datasets than those needed to train Deep Learning models. As a result, this efficiency factor is particularly useful in areas where data is limited, difficult to obtain or expensive to produce.
4. Shorter training times :
Machine Learning models are less complex and require less data. As a result, they can be trained much faster than Deep Learning models. This is of particular interest in time-sensitive environments.
5. Fiability & Reliability
In situations where training data is noisy or incomplete, Machine Learning models are often stable and robust. On the other hand, deep neural networks are susceptible to overlearning when training data is limited. Consequently, this phenomenon can affect model generalization.
In other words…
While Deep Learning provides powerful capabilities for learning complex representations from raw data, Machine Learning offers considerable advantages in the field of image analysis.
These include interpretability, quantity of training data, training time, computing power, stability and, last but not least, flexibility.