Neural networks have become incredibly popular in recent years for their ability to accurately model complex data. Yet, there are several disadvantages to using neural networks.
As a data scientist, you should be aware of them before deciding whether or not they are the right tool for the job.
We'll also discuss alternatives to neural networks that may be a better fit for your data.
A bit about neural networks
Neural networks are a type of machine learning algorithm that we use to model complex data.
They are composed of many interconnected processing nodes or neurons. These nodes can learn to recognize patterns of input data. It's analogous to how a human brain can learn to recognize patterns.
One of the advantages of neural networks is that they are very flexible, and we can use them for various tasks. They are also very scalable, meaning we can train them on massive datasets.
But, neural networks also have some disadvantages. This is the focus of this post.
1. Artificial Neural Networks require lots of computational power.
Neural networks are modeled after the brain and are composed of many interconnected processing nodes. Each node computes based on its weight parameters and adjusts them through backpropagation. Due to many parameters, ANN also needs more extensive datasets for training. ANN requires high computation power for these reasons.
Besides upgrading your hardware, you could also play around with the hyperparameters of your neural network. This could potentially help you reduce the amount of computational power required.
You could alter the batch sizes and epochs of ANN training to improve your training time and optimize computational resources.
A larger batch would train the network faster per epoch but need more memory. Smaller batch sizes would take more epochs to train the network but require less memory.
2. Neural network models are hard to explain.
It's relatively straightforward to explain traditional machine learning models. For instance, a linear regression model is easy to interpret because it's just a straight line. Coefficients of the line can be interpreted as the relationship between predictor variables and the response variable.
But, neural networks are much more complex. They are composed of many interconnected processing nodes. It isn't easy to understand how the node weights result in the predicted output.
If you need to generate model results that are easy to explain to a non-technical audience, neural networks may not be the best choice.
There are some ways to try and understand neural networks. But, they are still relatively opaque compared to other machine learning models.
A study by Zebin Yang et al. has introduced architectural constraints to the model. It results in an explainable neural network with a better balance between prediction performance and model interpretability.
3. Neural network training requires lots of data.
Neural networks are very flexible and can learn to recognize input data patterns.
This flexibility comes at a cost. Neural networks require lots of data to train. They are not able to generalize from limited training data. On smaller datasets, neural network models tend to overfit. They memorize the training data and don't generalize well to new examples.
A simpler machine learning model will perform better than a neural network on small datasets.
We can try to address this issue by using transfer learning. This is when we use a pre-trained neural network model and fine-tune it for our specific dataset.
This can work well if our dataset is similar to the dataset used to train the pre-trained model. But, if our dataset is very different, it's unlikely that transfer learning will be successful.
You could also try using a neural network with fewer parameters. This would reduce the amount of data required to train the model. But, it would also likely result in a less accurate model.
Another technique is to use data augmentation. This is when we create synthetic data by modifying our existing data.
For example, we could rotate an image at different angles or crop it differently. This would give the neural network more data to learn from without collecting new data.
4. Data preparation for neural network models needs careful attention
Data preparation is a crucial step in machine learning. It's essential for neural network models.
This is because neural networks are susceptible to input data.
If the data is not scaled correctly, it can result in a suboptimal model. To avoid this issue, you can use standardization or normalization techniques.
Also, if the input dataset is imbalanced, it can cause the neural network to learn patterns that are not representative of the real world. This could ultimately lead to a less accurate model.
5. Optimizing neural network models for production can be challenging.
You can build neural networks quickly with libraries such as Keras. But, once you've created the model, you need to think about how to deploy it in production.
This can be challenging because neural networks can be computationally intensive.
If you're not careful, it can result in a slow and unusable model. To avoid this, you need to optimize the model for production carefully. This includes techniques such as using parallelism, distributing computations, and reducing memory usage.
Such tasks often need a specialized optimization engineer role.
Neural networks are a powerful machine learning technique. But they have some limitations that you need to be aware of.
If you're working with small datasets, neural networks may not be the best choice. And, if you need to generate model results that are easy to explain, neural networks may also not be the best choice.
In this post, we've looked at some disadvantages of neural networks. But we've also seen that there are ways to overcome some of these issues.
If you're careful about data preparation and model optimization, you can use neural networks successfully in your machine-learning projects.