Hello and welcome to the journey of understanding and implementing neural networks using Python! Neural networks play an essential role in machine learning and AI, paving the way for groundbreaking innovations in numerous fields. By the end of this lesson, you will be able to create and define a simple neural network using Keras in TensorFlow, and understand the components of a neural network, their layers, and the role of weights, biases, and activation functions.

Neural networks are computational systems inspired by the human brain. They consist of neurons (the most basic unit), which are assembled in layers to make the network. Each neuron in one layer is connected to neurons of the next layer through synaptic weights. Moreover, each neuron has a bias that allows shifting the neuron's activation threshold.

An activation function regulates the output of a neuron given a set of inputs and the weights associated with them. One popular activation function we will be using is the ReLU (Rectified Linear Unit) activation function. Notably, neurons and layers play essential roles in neural networks. So, let's understand them in detail while learning to build our neural network.

Visualizing a simple neural network with an input layer, a hidden layer, and an output layer:

In the above image, the input layer receives the data, the hidden layer processes it, and the output layer provides the final result. The hidden layer is where the magic happens, as it transforms the input data into a form that can be used to make predictions.

In the graphical representation, each circle represents a neuron, and the lines connecting them represent the weights. The weights are adjusted during the training process to minimize the error in the model's predictions.