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Deep Learning
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Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is **inspired by the human brain**. 
Deep learning models can **recognize complex patterns** in pictures, text, sounds, and other data to produce accurate insights and predictions

* deep learning algorithms can be regarded both as a sophisticated and **mathematically complex evolution of machine learning algorithms**  

*  Describes algorithms that analyze data with a logic structure similar to how a human would draw conclusions.  

*  Can be supervised, unsupervised, semi-supervised, self-supervised, or reinforcement.

*  To achieve this deep learning applications use a layered structure of algorithms called an artificial neural network (ANN).


Artificial Neural Network (ANN)
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* Hidden layers are called hidden, because their values aren’t directly observed in the training data (they are calculated during the training process).  

* In simple terms, the hidden layers are calculated values used by the network to make predictions.  

* The more hidden layers a network has, the more complex the patterns it can recognize.  

* ANNs are hungry for data. The more data you feed them, the better they perform.  

.. image:: /files/images/ann.png
   :alt: Artificial Neural Network (ANN)


Advantages of deep learning:

* Automatic feature extraction (no need to manually define features)

* High accuracy


CNN (Convolutional Neural Network)
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CNNs are a type of deep learning neural network that have been successfully applied to analyzing visual imagery.

.. image:: /files/images/cnn.png
   :alt: Convolutional Neural Network (CNN)