Machine Learning vs Deep Learning

Machine Learning allows us to automatically learn a series of complex rules from labeled data or examples in a more accurate and faster way than trying to program them one by one.   

So we can say that Machine Learning is used in order to be able to explore how computers can program themselves and improve over time when getting more experience.  Hence, with machine learning and statistical methods: Machine Learning algorithms can make conclusions based on previous data. 

This technology is especially useful while detecting certain patterns that are almost impossible to detect by humans.  Like the human natural learning process, machine learning depends a lot of the training data and the amount of data; the more examples the system has the more precise its results will be.

To put things into perspective, here are some daily applications of machine learning:

• • • • Recommendation systems
      • Fraud detection
      • Spam detection
      • Forecasts and predictions
      • Natural language processing
      • Image processing

Deep Learning

Deep learning is a type of machine learning. It uses neural networks as a learning method.

Most common applications of deep learning:

CNN: Convolutional neural networks are often used for image applications.

RNN: Since language is most naturally represented as sequence data we can use Recurrent Neural Networks for natural language processing.

One of the key advantages of using Deep learning is that, in order to train the neural network, you just need the input and the output, the neural network will pretty much figure out the middle by itself. To do so you will need to train it using lots of data.

The advantage of deep learning over machine learning is that it includes an automatic feature learning phase for the phase of supervised training. This phase its a hierarchy go multiple layers, that cascades and feed each other in every iteration.