Unless you’ve been living under a rock for the last five years, you have likely heard about neural networks. Despite the confusion about them being inspired by the human brain, and certainly overhyped about in certain areas, neural networks have inspired a vast amount of research and have created a new field of artificial intelligence called deep learning.
Advantages of Neural Networks
There are numerous advantages to neural networks. As will be described below, reasons that one may choose a neural network over another type of machine learning algorithm might include the ability to add arbitrarily many hidden layers or ease of implementation combined with GPU acceleration.
A huge advantage of neural networks is that non-linearity is introduced to the network through the activation function. Typically, the activation function will be a sigmoidal function which smushes the input to the function to a range between 0 and 1. If non-linearity was not introduced, the model would simply be fitting straight lines (albeit in many dimensions) through the data.
Neural networks can have numerous layers stacked on top of each other. This allows learning of patterns that are deep. Hence the reason why the field of deep learning was spawned after the creation of deep neural networks. Since an endless number of layers can be added, neural networks are great candidates to deal with the ever-increasing amount of data that our world generates. With an estimated 163 zettabytes of data present in the world in the year 2025, models are needed that can scale seamlessly.
This is one of the real advantages to neural networks is that model performance increases as availability of data to the model increases.
Additionally, the accuracy increases greatly with neural networks as more data is fed to the model as shown in the diagram below:
Number of applications
The number of applications that are available to neural networks is immense. Already, they are being used in just about every field. There are examples of a convolutional neural network performing better than trained dermatologists in identifying skin cancers.
Neural networks perform extremely well in natural language processing tasks such as sentiment analysis of Amazon reviews where an accuracy of over 97% was achieved in classifying positive or negative reviews.
There are many more applications that could be discussed where neural networks are changing the landscape, but there is not nearly enough space on the page.
One of the main ways that neural networks are arguably the best all-around model to try and apply to huge, complex datasets is the fact that they can implemented extremely easy. With an abundance of libraries such as PyTorch and Keras, creating a neural network is a breeze. Another huge advantage of implementing neural networks is the GPU acceleration that has been built in with most of these libraries. Accelerating tensor computation on a GPU can speed up training up to 50x compared to CPU computations.
Implementation in Keras is as simple as 21 lines of code (this includes comments) as demonstrated by Machine Learning Mastery:
Similarly, creating a neural network in PyTorch is easy too as shown in this example from the PyTorch documentation:
Even though there are many advantages to neural networks, no model is perfect. There are certainly disadvantages to these types of models.
I would argue that some of the advantages are how easy, the model is to implement. As described in this article, neural networks are so widespread and can fit so many different types of problems that researchers broadly apply them in cases there perhaps they shouldn’t. Neural networks and especially some of the more complicated implementations of them such as convolutional neural networks or long-short term memory networks tend to exhibit a blackbox nature.
Deep understanding of how these systems work will allow users to make informed decisions about when and why a neural network should be used problems.
Training time and complexity
Another disadvantage of neural networks is perhaps due to 3.1, where neural networks aren’t always the right model choice for your data set. Sometimes, there simply is not enough data to train a neural network on. More data needs to be collected, or a simpler model should be used instead.
Neural networks can require more data than traditional machine learning algorithms due to the complexity of their error surfaces. In order to find the local minima of the error surface for a complicated multi-layer neural network, large amounts of data is needed so that the surface does not get thrown off by noisy data points or outliers.
Neural networks are the most popular artificial intelligence algorithm currently. They have a vast number of diverse applications combined with ease of implementation. The model allows modeling of highly complicated datasets to learn those hidden insights within the data. Application of neural networks to datasets needs to be done with caution since it can be easy to add too much power to the model and overfit. However, even though there are disadvantages, the advantages are abundant, and researchers continue to rely on them for discovery in the field of artificial intelligence.