Write a python code to draw neural network architecture
You can use a library such as `matplotlib` or `plotly` to create visualizations of neural networks.

Here is an example code using `matplotlib`:

```python
import matplotlib.pyplot as plt

def draw_neural_network(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotlib.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_network(fig.gca(), 0.1, 0.9, 0.1, 0.9, [4, 7, 2])
    
    :parameters:
        - ax : matplotlib.axes.AxesSubplot
            The axes on which to plot the cartoon (get e.g. by plt.gca())
        - left : float
            The center of the leftmost node(s) will be placed here
        - right : float
            The center of the rightmost node(s) will be placed here
        - bottom : float
            The center of the bottommost node(s) will be placed here
        - top : float
            The center of the topmost node(s) will be placed here
        - layer_sizes : list of int
            List of layer sizes, including input and output layer
    '''
    n_layers = len(layer_sizes)
    v_spacing = (top - bottom)/float(max(layer_sizes))
    h_spacing = (right - left)/float(len(layer_sizes) - 1)
    # Nodes
    for n, layer_size in enumerate(layer_sizes):
        layer_top = v_spacing*(layer_size - 1)/2. + (top + bottom)/2.
        for m in range(layer_size):
            circle = plt.Circle((n*h_spacing + left, layer_top - m*v_spacing), v_spacing/4.,
                                color='w', ec='k', zorder=4)
            ax.add_artist(circle)
    # Edges
    for n, (layer_size_a, layer_size_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
        layer_top_a = v_spacing*(layer_size_a - 1)/2. + (top + bottom)/2.
        layer_top_b = v_spacing*(layer_size_b - 1)/2. + (top + bottom)/2.
        for m in range(layer_size_a):
            for o in range(layer_size_b):
                line = plt.Line2D([n*h_spacing + left, (n + 1)*h_spacing + left],
                                  [layer_top_a - m*v_spacing, layer_top_b - o*v_spacing], c='k')
                ax.add_artist(line)

fig = plt.figure(figsize=(12, 12))
ax = fig.gca()
ax.axis('off')
draw_neural_network(ax, 0.1, 0.9, 0.1, 0.9, [4, 7, 2])
plt.show()
```

This code will draw a simple neural network with an input layer of 4 nodes, a hidden layer of 7 nodes, and an output layer of 2 nodes.
Explain what a hidden layer is and how to use and optimize it. Use common best practices in your explanations.
In a neural network, a hidden layer is a layer of neurons that is situated between the input and output layers. The neurons in the hidden layer perform complex computations on the input data and pass the results to the output layer. The term hidden refers to the fact that the internal workings of the layer are not visible to the outside world.

A neural network with multiple hidden layers is called a deep neural network. Deep neural networks are particularly effective at learning complex patterns in data and are widely used in various applications, such as image recognition, natural language processing, and speech recognition.

Here are some best practices for using and optimizing hidden layers in a neural network:

Choose the right number of hidden layers: Generally, a single hidden layer is sufficient for most tasks, but for more complex problems, multiple hidden layers may be necessary. However, adding too many hidden layers can lead to overfitting, so it's important to strike a balance between model complexity and generalization.

Choose the right number of neurons: The number of neurons in the hidden layer(s) also affects the performance of the neural network. Too few neurons may not capture all the relevant features of the data, while too many neurons can lead to overfitting. A common rule of thumb is to choose a number of neurons that is somewhere between the number of input and output neurons.

Use appropriate activation functions: The choice of activation function for the neurons in the hidden layer(s) can have a significant impact on the performance of the neural network. Common choices include ReLU, sigmoid, and tanh functions, each with its own strengths and weaknesses.

Regularize the network: Regularization techniques, such as dropout and L1/L2 regularization, can help prevent overfitting in deep neural networks by reducing the complexity of the model.

Optimize the network: There are various techniques for optimizing the neural network's parameters, such as stochastic gradient descent and its variants, which update the weights of the neurons based on the error between the predicted output and the actual output. Other techniques, such as batch normalization and weight initialization, can also improve the performance of the network.

Overall, hidden layers are a crucial component of deep neural networks and can greatly enhance the model's ability to learn complex patterns in data. By following best practices and experimenting with different configurations, you can optimize the performance of your neural network and achieve better results in your machine learning tasks.