It’s a pertinent question. There is no shortage of machine learning algorithms so why should a data scientist gravitate towards deep learning algorithms? What do neural networks offer that traditional machine learning algorithms don’t?

Another common question I see floating around – neural networks require a ton of computing power, so is it really worth using them? While that question is laced with nuance, here’s the short answer – yes!

The different types of neural networks in [deep learning], such as convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), etc. are changing the way we interact with the world. These different types of neural networks are at the core of the deep learning revolution, powering applications like unmanned aerial vehicles, self-driving cars, speech recognition, etc.

It’s natural to wonder – can’t Machine Learningalgorithms do the same? Well, here are two key reasons why researchers and experts tend to prefer Deep Learning over Machine Learning:

- Decision Boundary
- Feature Engineering

Curious? Good – let me explain.

**1. Machine Learning vs. Deep Learning: Decision Boundary**

Every Machine Learning algorithm learns the mapping from an input to output. In case of parametric models, the algorithm learns a function with a few sets of weights:

```
Input -> f(w1,w2…..wn) -> Output
```

In the case of classification problems, the algorithm learns the function that separates 2 classes – this is known as a **Decision boundary** . A decision boundary helps us in determining whether a given data point belongs to a positive class or a negative class.

For example, in the case of logistic regression, the learning function is a Sigmoid function that tries to separate the 2 classes:

As you can see here, the logistic regression algorithm learns the linear decision boundary. It cannot learn decision boundaries for nonlinear data like this one:

Similarly, every Machine Learning algorithm is not capable of learning all the functions. **This limits the problems these algorithms can solve that involve a complex relationship.**