Machine learning (ML) and deep learning (DL) are both subcategories of AI.
Well, let’s first start off by explaining the need for machine learning. A question was raised in the statistical world, how can we efficiently train large complex models? How can we train more robust versions of the AI systems? Machine learning borrows models and methods from statistics and probability theory. So in other words, machine learning uses statistic methods to enable machines to improve with experience. For example, linear regression gives us the ability to show the predicted value and estimated value, which in theory shows us how precise the model is. Deep learning is a particular kind of machine learning that is inspired by the functionality of the brain, hence the popular model, artificial neural network.
Below are the major difference between ML and DL
- In machine learning, we must provide a set of inputs, such as features to identify, for instance, a cat from a dog. Whereas in deep learning, the neural network picks out the features without human interaction. This means we must input large volumes of data so analysis and learning can take place.
- Other areas that are important to highlight include computational power. Since large volumes of data can take up considerate amount of computational power. In deep planning, the performance increases as the volume of data increases. Whereas for machine learning, as the data increases the performance stabilizes at a given point.