
While there are many types of neural networks the most common networks that will be typically seen are: 

- Feedforward neural networks
- Convolutional neural networks
- Recurrent neural networks

Types of Neural Networks

Python Neural Network Class

When working with Neural networks, it involves quite a bit a math to determine the weights and bias. This is so the computer can accurately determine the output. But in order to really figure that out. Fine tuning of the weights and bias from the input data is known as "training" the neural network.

Training Neural Networks

Breaking this down into to components:  

- What are Neurons? 
- How are they connected?


 - At first, we can think view a neural network as how our brains work like neurons signaling one another (Also the inspiration for its name). In a sense these models allow computer programs to recognize patterns and solve common problems.

- Neural Networks are comprised of node layers. It contains the input layer, one (or more) hidden layers, and an output layer.

- Nodes (neurons) are connected via arrows and each has an associated weight and threshold. If the node surpasses the threshold set, then that neuron is activated and the data is sent on the next layer. This process repeats until it goes to the output layer.


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- Neural Networks
- Forward Propagation
- Backward Propagation
- Activation Functions

Foundational Concepts of Deep Learning

Neural Network

In simple terms, an activation function is added in the neural network in order to help the network learn complex patterns in the data. When comparing this to the brain a neuron sends signals to other neurons but what signals being sent depends on the conditions. The activation function is at the end taking in the data from the node and transforms it into an input that a new node (neuron) can take into the new layer.

- And of course, typically math is involved with coming up with these functions.

Activation Functions

As the name suggests, it simply means that the input data is pushed in the forward direction of the network. The data will never flow in the reverse direction during the output generation.

To help further this idea: consider using a feed-forward neural network.


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