In between the input layer and the output layer are hidden layers. This is what separates neural networks and deep learning. A basic neural network probably has one or two hidden layers, while a deep learning network might have dozens, or even hundreds, of these layers.
Increasing the number of different layers and nodes helps increase the accuracy of a network. For example, if a neural network is trained to recognize images of birds, more layers will enable more precise results. Such a network will not just be able to distinguish a crow from a chicken, but also a crow from an eagle.
“Deep learning models are highly effective at recognizing patterns because they process information in layers,” says Sukh Sohal, Senior Consultant at Affinity Reply.
He explains that each layer focuses on different aspects of the data, from simple features like edges in an image to more complex structures like objects. “This layered approach allows the model to identify patterns in raw, unstructured data without manual input, making it particularly valuable for image recognition, speech processing, and natural language understanding,” says Sohal.
Benefits of deep learning
One major benefit of deep learning is that its neural networks can reveal hidden insights and relationships in data that weren’t previously visible. Modern-day robust deep learning models can help businesses analyze large, complex data thanks to the following abilities:
Process multiple data types: Deep learning systems can process both structured and unstructured data. For example, in addition to carefully constructed and labeled surveys, these systems can, for instance, be trained to look at textual data by analyzing social media posts, to provide customer insights.
Data scalability: Deep learning models are also known to perform increasingly well as the volume of data grows. This is in stark contrast to traditional machine learning algorithms, which can reach a performance plateau after a certain threshold.
Pattern discovery: Deep learning systems can analyze large amounts of data and uncover complex patterns or derive insights that they might not even have been trained on.
Feature engineering: A deep learning algorithm can save time because it doesn’t require humans to extract features manually from raw data.
Efficiency: When a deep learning algorithm is properly trained, it can perform an order of magnitude faster than humans.
How deep learning works
As we’ve said before, deep learning models have multiple layers of interconnected nodes, with each layer building upon the last to refine predictions and classifications. Each node in a layer takes the data from the previous layer, processes it, and passes it to the next layer.
The data is transformed at each layer through complex mathematical operations. The nodes apply activation functions to perform nonlinear transformations to their input and use what they learn to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. This is known as forward propagation.
But before deep learning models can be pushed into action, they need to be trained to identify patterns from the input data. This is where the concept of backpropagation comes in. After forward propagation, the model makes a prediction. The difference between the predicted output and the actual value is known as the error.
Backpropagation works by propagating this error backward through the network. It adjusts the network’s weights, and other parameters that influence the output, in a bid to improve the accuracy of its predictions.
The model goes through multiple iterations or epochs, wherein each epoch, the data passes through the network, and the weights are adjusted to reduce the error. Over time, the network learns to make more accurate predictions.
Another thing to note is that in traditional machine-learning models, the learning process is supervised. That is to say, a human programmer tells the computer what features it should look for in order to say, for instance, whether an image is that of a cat or not. This process, called feature extraction, takes time, and the success of the model depends on the programmer’s ability to accurately define a feature set for the target.
The advantage of deep learning is that the program builds the feature set all by itself through unsupervised learning.
For instance, the model is fed with training data made up of labeled images identifying cats and other animals. The deep learning model uses this to create a feature set for a cat and builds a predictive model based on this. Initially, it might classify everything with four legs and a tail as a cat. But with each iteration, the predictive model will improve and become more accurate and will learn to distinguish cats from dogs.
Key uses of deep learning
Because deep learning models process information in ways similar to the human brain, they can be applied to virtually all the tasks that we do. Deep learning is currently used most commonly for image recognition, natural language processing (NLP), and speech recognition.
Digital assistants, like Siri, Cortana, and Alexa, are some of the most common examples of deep learning. They use NLP to respond to questions and adapt to user habits.
In fact, their capabilities, like computer vision, don’t just help facilitate autonomous transportation but are also a big aid in the manufacturing sector. Deep learning can monitor processes, detect anomalies, and help identify quality problems. This in turn helps save money from unplanned downtime and also helps improve efficiency.
Some of the common use cases for deep learning include all types of big data analytics applications, such as language translation , medical imaging and diagnosis, stock market trading signals, network security, and more. Retailers for instance can use customer data to predict customer preferences and needs and stop housing needless stock.
Similarly, application developers can use deep learning to track user behavior and generate customized suggestions to help them discover new products and services. In fact, several streaming services such as Netflix use deep learning to provide personalized video recommendations.
Deep learning models are also used in the financial sector for everything from developing trading strategies to predicting stock prices. They are also increasingly being used to spot security threats and protect against fraud. As far as analyzing trends is concerned, deep learning is also making inroads in the healthcare sector to predict illnesses. They can also build on this to help healthcare workers devise optimal tests and treatments for patients.
“A growing trend is their use in sectors like agriculture, where they help identify crop diseases and improve yields,” adds Sohal. “This versatility ensures that deep learning continues to expand into new and unexpected areas, transforming industries along the way.”
