Recent developments in artificial intelligence (AI) have been focused on neural networks and deep learning. These strategies are inspired by how neurons work in our brains!
Concepts such as feedforward neural networks, backpropagation of error, stochastic optimization, batch training, dropout, convolutional layers, etc., all play an important role in making neural network models more powerful.
The term “neural” comes from the word neuron because these systems imitate how they function in nature. There is much interest in applying this technology to problems that require pattern recognition, classification, and regression.
This article will go over the basics of both neural net architectures and some fundamental concepts of deep learning with Python. You will also learn about different types of neural nets, what activation functions to use, and how to implement them using Keras, one of the most popular AI libraries for beginners.
Keep reading for helpful resources and links.
Types of neural networks
There are three main types of neural network you can learn about when studying neural networks with deep learning. These are feed-forward, convolutional, and recurrent neural networks.
Feed-forward networks are one of the most common types of neural networks used for image classification. They work by taking input images and breaking them down into layers that perform specific functions. The layers are connected to each other in such a way that information is passed along from layer to layer. Some examples of this are the VGG net or Google’s Inception v3 architecture.
Convolutional nets also go through several stages where they take an input and then apply filters onto it. This process is repeated until a full picture has been formed. An example of this would be the famous Alexnet which was made popular back in 2012. More recent architectures like those mentioned above use more advanced techniques such as pooling and batch normalization before applying their convolutions.
Recurrent neural networks work similarly to convolutional ones except there isn’t any kind of filtering done. Information just flows in a circle instead! A classic example of this type of network is something called LSTM (Long Short Term Memory) which looks at past data to make predictions.
Neural Networks — What Are They?
Neural networks are computer algorithms designed to do things requiring thinking and patterns.
Feed-forward neural networks
Feed-forward neural networks are one of the most fundamental types of neural network architectures. They contain an input layer, hidden layers, and an output layer, just like any other neural network. The key difference is that there are no outputs from the final layer to create a prediction.
Instead, the last layer becomes an activation function. This functions nonlinearly on the inputs it receives in order to produce new values as outputs.
The most common activation functions used for this purpose are sigmoid (like the one we used for our first example) or hyperbolic tangent (tanh).
Once these functions have been applied, they can be mixed and matched with almost any kind of input data! That’s why feed forward nets are so powerful – you get access to all sorts of functions with your architecture.
There are many ways to use deep learning with feed forward nets, but one of the more popular uses is image classification.
Recurrent neural networks
Recurrent neural networks (RNNs) are an advanced machine learning technique that have become increasingly popular in recent years. RNNs can be thought of as sequential pattern matching machines! This is interesting to note because they resemble the way our brains work.
Think about it- when we learn new things, we repeat patterns and sequences of actions over and over again. For example, if you asked someone who learned how to swim for their first time whether they could remember anything else once they got into the pool, they would say no!
This makes sense though, right? You’d probably forget how to breathe or even dive underwater! So, by that same token, RNNs may not teach your model any more specific concepts until it has repeated those patterns enough times.
By this logic, one might assume that it would take longer for computers to “learn” certain tasks than humans do! Luckily, technology has caught up with this theory and there are now computer programs that perform just as well if not better than human experts in many difficult tasks like speech recognition, image classification and natural language processing.
Convolutional neural networks
CNNs are one of the most influential concepts in computer vision today. They consist of several layers that learn to recognize patterns in data. For example, let’s say you want your model to identify pictures of dogs. The first layer of the network would be figuring out what kind of shape some part of the dog is. A second layer would look at how those shapes connect with each other to determine if it is a face or not. A third layer would consider color information.
A fourth layer would analyze texture and patterning. And finally, a fifth layer would compare the overall structure of the dog against known images of canines.
The best modern convolutional neural networks have very many such interconnected layers. Some use up to 100 individual weights (the numbers under a neuron refer to its weight). By having so many connections, they can find fine-grained detail in an image.
Deep neural networks
Recent developments in deep learning involve replacing part of the network called the layers with what are known as “deep” networks or “neural networks.” The layer in question is typically referred to as an “activation function,” because it activates (or not) other neurons within the network depending on its input.
The activation functions most commonly used include the sigmoid, hyperbolic tangent, softmax, and tanh. These functions all have their strengths and weaknesses, but they all push hard for one result either 1 or 0. Some will produce very close-to-zero results even when asked a difficult question, while others may overcompensate by giving almost-certain answers.
With that being said, we can now create our first neural network! Let’s take a look at how.
Applications of neural networks
Recent developments in artificial intelligence use concepts from neuroscience to make computers function with pattern recognition and reasoning. These systems are called neural network or deep learning algorithms.
Neural networks were inspired by how neurons work as part of our brain’s circuitry. Just like humans, some parts of the nervous system respond more quickly to certain stimuli than others. Computers now employ these timing differences to perform tasks automatically.
For instance, when you look at the sky, your eye takes about one hundred fifty milliseconds (150 ms) to detect light.
How neural networks work
Neurons are an integral part of our brains. They play a large role in how we process information, making connections with other neurons to form patterns and understanding relationships.
Neural networks use these similarities to learn new tasks by linking individual neurons together into groups or layers. These layers are connected to each other and to input and output neurons.
When you give a neuron input data it processes this information using internal algorithms (for instance, finding edges in pictures) then transmits this processed information to another neuron through a connection called a synapse.
The way the network uses all of its parts comes down to what it is being asked to do. For example, if there’s a picture of your friend and a dog, the image recognition software in the brain will compare both images with every memory it has of your friends and dogs.
It will pick up on their facial features, body shape, tail style, etc., and match them to the ones in the new photo to determine which type of animal they are. The more memories it has, the better it will perform computer vision!
By having multiple interconnected layers of neurons, neural networks can achieve very complex results that would otherwise take many different types of computers working alone to produce. This is why they have become so popular in recent years.
There are several types of neural networks, but one of the most well-known is the feedforward neural netowrk.
Examples of neural networks
Neurons are the building block of our brains. They play an integral part in how we perceive, process, and understand information around us.
Neural networks are no different!
By incorporating concepts such as neurons into your understanding of deep learning, you’ll be able to apply these principles beyond just computer vision or natural language processing. You can use them to solve all sorts of problems involving pattern recognition and knowledge representation.
There are many examples of applications for neural networks in various fields, some more advanced than others. Some simple ones include creating pictures using artificial intelligence or finding patterns in large datasets.
Here I will go over several easy ways to implement a basic feed-forward neural network in Python. After that, we’ll move onto perceptron networks and multilayer perceptrons before exploring recurrent neural networks and gated architectures.
What is a feed-forward neural network?
A feed-forward neural network (FFNN) is a type of neural network where input data flows through multiple layers of nodes connected with weighted edges. Each layer takes the output from the previous one and processes it according to learned parameters, then passes this processed result along to the next layer.
The last layer produces the final outcome by combining all inputs together via multiplication. This way, each node in a given layer only receives raw values but does so combined with other nodes in the same layer and eventually the outputs you want.