NEURAL NETWORKS
Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.
WHAT ARE NEURAL NETWORKS ?
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
HOW DO NEURAL NETWORKS PRACTISE ?
Once the network has been trained with enough learning examples, it reaches a point where you can present it with an entirely new set of inputs it’s never seen before and see how it responds. For example, suppose you’ve been teaching a network by showing it lots of pictures of chairs and tables, represented in some appropriate way it can understand, and telling it whether each one is a chair or a table. After showing it, let’s say, 25 different chairs and 25 different tables, you feed it a picture of some new design it’s not encountered before — let’s say a chaise longue — and see what happens. Depending on how you’ve trained it, it’ll attempt to categorize the new example as either a chair or a table, generalizing on the basis of its past experience — just like a human. Hey presto, you’ve taught a computer how to recognize furniture!
That doesn’t mean to say a neural network can just “look” at pieces of furniture and instantly respond to them in meaningful ways; it’s not behaving like a person. Consider the example we’ve just given: the network is not actually looking at pieces of furniture. The inputs to a network are essentially binary numbers: each input unit is either switched on or switched off. So if you had five input units, you could feed in information about five different characteristics of different chairs using binary (yes/no) answers. The questions might be 1) Does it have a back? 2) Does it have a top? 3) Does it have soft upholstery? 4) Can you sit on it comfortably for long periods of time? 5) Can you put lots of things on top of it? A typical chair would then present as Yes, No, Yes, Yes, No or 10110 in binary, while a typical table might be No, Yes, No, No, Yes or 01001. So, during the learning phase, the network is simply looking at lots of numbers like 10110 and 01001 and learning that some mean chair (which might be an output of 1) while others mean table (an output of 0).
TYPES OF NEURAL NETWORKS.
Neural networks can be classified into different types, which are used for different purposes. While this isn’t a comprehensive list of types, the below would be representative of the most common types of neural networks that you’ll come across for its common use cases:
The perceptron is the oldest neural network, created by Frank Rosenblatt in 1958. It has a single neuron and is the simplest form of a neural network:
Feedforward neural networks, or multi-layer perceptrons (MLPs), are what we’ve primarily been focusing on within this article. They are comprised of an input layer, a hidden layer or layers, and an output layer. While these neural networks are also commonly referred to as MLPs, it’s important to note that they are actually comprised of sigmoid neurons, not perceptrons, as most real-world problems are nonlinear. Data usually is fed into these models to train them, and they are the foundation for computer vision, natural language processing , and other neural networks.
Convolutional neural networks (CNNs) are similar to feedforward networks, but they’re usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.
Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithms are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.
NEURAL NETWORKS VS. DEEP LEARNING
Deep Learning and neural networks tend to be used interchangeably in conversation, which can be confusing. As a result, it’s worth noting that the “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers — which would be inclusive of the inputs and the output — can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.
HOW ARE COMPANIES USING NEURAL NETWORKS ?
Companies are using neural networks in various ways, depending on their business model.
“LinkedIn for instance, uses neural networks along with linear text classifiers to detect spam or abusive content in its feeds when it is created,” explained Deepak Agarwal, LinkedIn’s vice president of Artificial Intelligence. “We also use neural nets to help understand all kinds of content shared on LinkedIn — ranging from news articles to jobs to online classes — so we can build better recommendation and search products for members and customers.”
DialogTech
DialogTech uses neural networks to classify inbound calls into predetermined categories or to assign a lead quality score to calls, Hoolihan said. The neural network performs these actions based on the call transcriptions and the marketing channel or keyword that drove the call, he said. “For example, a caller who is speaking with a dental office may ask to ‘schedule an appointment.’ The neural network will seek, find and classify that phrase as a conversation, therefore providing marketers with valuable insights into the performance of marketing initiatives.”
THE FUTURE FOR NEURAL NETWORKS .
With the size of neural networks increasing at a considerable pace (doubling every 2.4 years ), the power of the technology to solve increasingly complex problems becomes more feasible. Provided that AI ethics are incorporated, the combination of larger neural networks, increasing processing power, larger datasets, and the results of decades of research offers an exciting future for the application of Artificial Neural Networks to benefit society.
