In the first part of this post, we discussed what neural network predictions are, what the "artificial" component in them is, and how they are used in data science.
Today, we look at how they are used in predictive analytics. We will also answer why neural networks are still not being used by many businesses. Read more about neural networks for prediction.
The Two Big Arguments Against Neural Networks
The two big arguments against using artificial neural networks are:
- They are resource-intensive
- Their results are often hard to interpret
On the other hand, neural networks in predictive analytics may be used for solving problems the human brain is very good at, such as recognizing sounds, pictures, or text.
They can be used to extract features from neural network prediction algorithms for clustering and classification, essentially making them modules of larger Machine Learning apps.
As we said in our earlier post, an artificial neural network (ANN) is a predictive model designed to work the way a human brain does. In fact, ANNs are at the very heart of deep learning.
The deep neural network model (DNN model) can group unlabeled data based on similarities existing in the inputs, or classify data when they have a labeled dataset to train on.
What's more, DNNs are also scalable and best suited for machine learning tasks. Using these, we can build very robust and accurate predictive models for predictive analytics.
Structure of A Neural Network Prediction
There are three layers to the structure of a neural network algorithm:
The Input Layer
This enters past data values into the next layer.
The Hidden Layer
This is a key component of a neural network. It has complex functions that create predictors. A set of nodes in the hidden layer, called neurons, represents mathematical functions that modify the input data.
The Output Layer
Here, the predictions made in the hidden layer are collected to produce the final layer, which is the model's prediction.
How Do Neural Networks actually predict?
Each neuron takes into consideration a set of input values. Each of them gets linked to a "weight", which is a numerical value that can be derived using either supervised or unsupervised training, such as data clustering, and a value called "bias".
The network chooses from the answer produced by a neuron based on its weight and bias.
Classification in Neural Networks
Where "Classification" is concerned, all such tasks are contingent on labeled datasets. This means that you need supervised learning.
Supervised Learning is where humans check to see if the answers the neural network gives are correct. This helps the neural network understand the relationship between labels and data.
Examples of this are face-detection, image recognition, and labeling, voice detection, and speech transcription. With classification, deep learning can associate pixels in an image with the name of a person.
Clustering in Neural Networks
"Clustering," or grouping, is the recognition of similarities. One must understand that the deep learning model does not always require labels to find similarities.
When there are no labels by helpful humans to learn from, it uses machine learning to learn on its own, which means unsupervised learning. This retains the potential of producing highly accurate models. Examples of clustering can be customer churn.
Use of Neural Networks in Predictive Analytics
As we all know, predictive analytics combines techniques like predictive modeling with machine learning to analyze past data to predict future trends.
But neural networks differ from regular predictive tools. The most often used model – linear regression – is actually a very simple way of going about things as compared to a neural network.
Neural networks work better at predictive analytics because of the hidden layers. Linear regression models use only input and output nodes to make predictions.
The neural network also uses the hidden layer to make predictions more accurate. That's because it 'learns' the way a human does.
So why doesn't everyone use neural network prediction? For one, they require massive amounts of computing power, so they are cost-prohibitive.
In addition, machine learning using neural networks works best when trained with extremely large data sets, which your business might not have. But with IT tech getting cheaper, the first hurdle may soon disappear. Soon, technology like ANNs will mean that there will be no more "unpleasant surprises".
Advanced Neural Network Applications
Deep Learning in Business Intelligence
Neural networks are revolutionizing how businesses approach data analysis:
- Pattern Recognition: Identifying complex patterns in customer behavior
- Anomaly Detection: Finding unusual patterns that might indicate fraud or errors
- Predictive Modeling: Forecasting future trends with high accuracy
- Natural Language Processing: Understanding and processing human language
Neural Networks in Customer Analytics
A customer data platform that "thinks" like humans? That's right. A good CDP provides a unified view of customers. But our CDP Oyster goes beyond that.
It uses neural networks to understand customer behavior much better than other CDPs. Oyster is trained to "learn and think" like the human brain. This means highly accurate predictions about customer behavior.
Types of Neural Networks
Feedforward Neural Networks
The most basic type of neural network is one where information flows in one direction from the input to the output layers.
Convolutional Neural Networks (CNNs)
Specialized for processing grid-like data such as images, CNNs use convolutional layers to automatically learn spatial hierarchies of features.
Recurrent Neural Networks (RNNs)
Designed for sequential data, RNNs have connections that form directed cycles, allowing them to maintain internal memory.
Long Short-Term Memory (LSTM)
A special kind of RNN capable of learning long-term dependencies, making them ideal for time series prediction and natural language processing.
Implementation Challenges and Solutions
Computational Requirements
Neural networks require significant computational resources:
- GPU Acceleration: Using graphics processing units for faster training
- Cloud Computing: Leveraging cloud platforms for scalable computing power
- Model Optimization: Techniques to reduce model size and complexity
Data Requirements
Large datasets are essential for neural network training:
- Data Augmentation: Techniques to artificially increase dataset size
- Transfer Learning: Using pre-trained models for smaller datasets
- Active Learning: Iteratively selecting the most informative data points
Interpretability
Making neural network decisions understandable:
- Feature Importance: Identifying which inputs most influence predictions
- Model Visualization: Creating interpretable representations of neural networks
- Explainable AI: Techniques to explain model decisions
Best Practices for Neural Network Implementation
Data Preparation
- Normalization: Scaling input data to improve training efficiency
- Feature Engineering: Creating meaningful input features
- Data Quality: Ensuring clean, consistent, and relevant data
Model Architecture
- Layer Design: Choosing an appropriate number and size of layers
- Activation Functions: Selecting suitable activation functions for each layer
- Regularization: Preventing overfitting through techniques like dropout
Training Process
- Learning Rate: Setting appropriate learning rates for optimal convergence
- Batch Size: Choosing suitable batch sizes for training efficiency
- Early Stopping: Preventing overfitting by monitoring validation performance
Future Trends in Neural Networks
Edge Computing
Deploying neural networks on edge devices for real-time processing and reduced latency.
Federated Learning
Training neural networks across decentralized data sources while maintaining privacy.
Neural Architecture Search (NAS)
Automated discovery of optimal neural network architectures for specific tasks.
Quantum Neural Networks
Exploring the potential of quantum computing for neural network training and inference.
Conclusion
Neural networks represent a powerful paradigm shift in predictive analytics, offering capabilities that traditional statistical methods cannot match. While they come with challenges in terms of computational requirements and interpretability, the benefits they provide in terms of accuracy and flexibility make them an essential tool for modern data science.
As technology continues to advance and computational resources become more accessible, neural networks will become increasingly prevalent in business applications. Organizations that embrace these technologies early will gain significant competitive advantages in their predictive analytics capabilities.
The key to successful neural network implementation lies in understanding both their capabilities and limitations, and in developing strategies to address the challenges they present. With proper planning and execution, neural networks can transform how organizations approach predictive analytics and decision-making.
