Neurons are one of the most important parts of any neural network architecture. These work by gathering input information from their surroundings and passing it onto other neurons for processing, or even outputting some kind effect like recognizing patterns or completing tasks.

The number and size of each neuron’s internal pool of neurotransmitters (dopamine, serotonin, etc.) directly correlates with how well the model is able to recognize patterns and learn new things.

Given that humans are made up of around 100 billion neurons and 10-15 trillion synaptic connections, experimenting with different types and numbers of neurons is very valuable. Luckily, there are several easy ways to test various architectures!

This article will go over five popular strategies to design novel neuronal structures in both convolutional and fully connected networks. By understanding these concepts, you can create your own net topologies quickly!

Disclaimer: While this article will talk about trained weights and biases, it should be noted that these are mostly not needed outside of academic settings. In practical applications, researchers have found pretrained models to perform just as well without them!

So why add unnecessary parameters? Because adding more parameters usually results in better accuracy, which is the whole point of using a deep learning algorithm in the first place!

Regularizers

A regularizer is an additional layer of weight we apply to our network to prevent it from training poorly.

K-means clustering

Clustering is one of the most fundamental concepts in mathematics and computer science. In cluster analysis, you take your data set and group elements into different clusters or categories depending on how similar they are to each other.

Neural networks use this concept as a building block to learn patterns from examples. When creating neural network architectures, one must first decide what category or cluster element their dataset should be divided into.

The easiest way to do this is to pick a categorical variable that contains distinct values. For example, if your dataset consists of pictures, you could choose to classify a picture according to its genre. Or if it’s determined by time of day, you can create an algorithm that learns when a pattern changes.

After choosing your categorical feature, you will need to determine how many discrete groups or clusters there should be in your model. This is called the “k” value for your classification task and depends on two things: how well the classes separate into different regions and whether there is natural division in your data.

Once these conditions have been met, you can begin grouping elements with k-mean clustering, which works by starting out with every item in the dataset in its own cluster.

Then, you calculate the mean (average) of all items in each cluster and move everything in the same cluster towards that average while moving everyone else to another cluster.

Hierarchical clustering

Another method for neural network architecture design is hierarchical clustering. This was one of the first types of architectures designed, and it still works very well when applied properly!

Hierarchical clusterings are an excellent way to organize information. They go hand in hand with other classification methods like k-means or self organizing maps.

A hierarchical clusterer takes input data and groups objects into clusters based on certain criteria. In this case, the criterion is something related to the object’s properties. For example, if each item is categorized by its size, then the algorithm can create a cluster based on items that have the same sized clothing.

Connected layers

Layer type is one of the most important parts of neural network architecture. There are two main layer types used in almost every popular net architecture – convolutional layers and fully connected (also called dense) layers.

Convolutional layers operate by sliding an internal feature map across part of the input image, producing a new set of features. The size of these filters is typically small, which means that only nearby regions of the picture get tuned into specific patterns or features.

Fully-connected layers take all of the information in the previous layer and combine it with all of the data from the next layer to produce the output.

The number of connections per neuron is usually very high, making this layer more powerful but also much slower to process large amounts of data.

Nested layers

One of the most important parts of designing neural network architectures is nesting, or layering your networks inside of other networks. This technique allows you to create very deep networks by stacking layer upon layer of input processing that get cascaded together.

The key part of this concept is that each successive layer learns more about its inputs through the preceding layers. For example, say you have an image classification task where your model must determine if a picture contains a dog or not. The first layer of the network will be given an image as input and it will perform some basic mathematical operations to extract features such as lines and curves.

These low level features are then passed onto the next layer which does similar things except now it’s looking for specific patterns like spots or whiskers. And so forth until the final layer is hit, which performs simple binary classifications (is it a dog or not?).

By having several levels of abstraction in your architecture, you can use the lower level features to make general predictions while higher level features help identify finer points of the image. In computer science terms, this is called “hiding” information at different scales!

This way you don't need perfect quality images at the highest level to work, you can instead take shortcuts and rely on the underlying structure of the images. By adding another layer of abstraction, you're also creating a potential opportunity to improve performance since we've made our problem easier.

Layers that should be grouped

Layer groups are an effective way to organize layers of your network. By grouping similar layers together, you can easily reorder or remove a layer without having to worry about what effect it will have on the rest of the model.

By looking at some common neural networks, we can see how important layer groups are. Take a look at Figure 1 below, which is an example of a convolutional neural network (CNN) with groupings. The first two boxes in the left column are each made up of one convolutional layer and one pooling layer.

These two layers work together to process spatial information by looking for patterns across different areas of the image. Once they’ve processed all the pixels, the next set of functions takes over and performs more sophisticated processing like recognizing objects and finding features such as edges or shapes.

The third box is another batch normalization layer followed by a fully connected layer. This layer connects every pixel to every other neuron using numerical weights, which help CNNs perform better by helping them learn more generalized concepts.

The last non-grouped layer is a softmax loss function, which produces a probability distribution over classes.

Layers that should be separated

Layer separation is an important part of designing neural network architectures. You want to make sure that your networks have enough layers to sufficiently train your model, but you also do not want too many layers because this can lead to overfitting or fat networks that are hard to tune.

When it comes down to it, there are two main reasons why having more than three layers is not good. First, adding new layers means that your net needs to spend more time to update its weights which could result in poor performance during training and testing.

Second, larger models take longer to run due to the additional math calculations needed per layer. This could hurt their efficiency for applications where speed is very critical like real-time chatbot services or medical diagnosis.

There are some cases when having several small independent networks within one large network makes sense. For example, if you wanted to classify images into different categories then we would use something called an ensemble method where all these smaller networks are trained separately and then combined together into one bigger classification block.

Dense or sparse

Choosing whether to use a dense network or a sparser one will depend mostly on the task at hand. If your image classification goal is to have many categories, then a denser model that has more connections between nodes is better.

Dense networks are also easier to train because they require less careful tuning of several hyperparameters. However, there are cases where it can be beneficial to have very few direct links between layers instead of lots!

For example, if you want to learn about products from images alone, you do not need every possible category in your product classifier. Having fewer general concepts makes it more likely that an image contains only information about one specific brand or type of product.

Sparse models work well when this is the case since you do not waste time training on classes that do not apply. In these situations, using a sparsity constraint may help prevent overfitting by requiring that at least one node connects to another node as opposed to being completely disconnected.

How to choose your activation function

Choosing an appropriate activation function for your network is one of the most important parts of designing neural networks.

There are many different types of functions that you can use as activations in your layers, with each having their own strengths.

Some examples of popular activation functions include the sigmoid (logistic), hyperbolic tangent (tanh), softmax, rectified linear unit (ReLU), leaky ReLu, and absolute value (sign).

Each of these functions has its pros and cons depending on the problem domain. For instance, the tanh activation works well when there are no infinite values that you want to prevent your model from predicting, while the softmax works well when there are discrete numbers to predict.