Tools and techniques like neural networks, generative adversarial networks, and data mining are used across application areas. These methods are constantly changing and evolving to return higher quality results in industry.
Neural networks are a subcategory of machine learning. They were originally modeled after our understanding of the behavior of neurons in the human brain: in the brain, a single neuron takes in input, processes it, and sends output. Neuroscience has moved away from this idea. We now know brains don’t actually work like this, and the statistics behind neural networks in machine learning have been developed independently of neuroscience.
Neural networks are typically created with layers that compute information in parallel. They’re composed of interconnected nodes. Knowledge in these systems is represented by the patterns that are taken on by nodes passing information to each other.
The way people think about the composition of a neural network usually includes three basic parts:
- Input layers: Contain input data
- Hidden layers: Contain the synapse architecture
- Output layers: Provide results from the network
Within that framework, a neural network can take on many architectures. Not all neural networks are the same. In the implementation, training is also an important part of the process. Training involves sending data through the neural network. In this stage, the network is learning complex connections between inputs and desired outputs. In many instances, the network’s effectiveness is reliant on high-quality data.
Understanding the basic mechanisms of neural networks helps provide a foundation for understanding how contemporary artificial intelligence works.
Generative Adversarial Networks
Unsupervised learning can be inefficient because machines must learn by themselves. What is obvious to us may not be obvious to a machine. Generative adversarial networks (GANs) are one way to increase the efficiency of unsupervised learning. GANs use two neural networks: one network generates results, and the other evaluates the accuracy of those results.
GANs are a more recently adopted technology in the machine learning space and have been proposed by companies such as Amazon as a method for creating AI fashion designers in 2017. These and other generative models are especially promising for creating unique new images as well as for filling in information from images that are incomplete or damaged.
Data is critical to any task in machine learning. Without data, the machine has nothing to train from. Data can include information such as video, images, and text. Data collection refers to the process of collecting data for analysis.
In many cases, data collection is just the beginning. What do you do with all the data? Data mining is about uncovering useful information in large amounts of data. For the fashion industry, social media can be a treasure trove for learning about the way customers feel about products and trends.