What is Deep Learning?

Deep learning is a branch of machine learning that relies on the analysis of numerous samples to develop knowledge, very similar to how humans learn. For example, when teaching a young child to identify a bird, various images of different species are shown to them. Over time, the child would be able to recognize birds, even if not all of them look alike.

This method utilizes multi-layer neural networks. To process the information they receive, such as images, these networks pass the data through interconnected nodes, starting from the input layer. As the information moves through each layer, each node performs simple mathematical operations before transmitting the information to the other nodes. This process is repeated until the data reaches the output layer.

Between the input layer and the output layer, there are the hidden layers, which are the distinguishing feature of deep neural networks. While a basic neural network may have one or two hidden layers, a deep learning model might have dozens or even hundreds of them. The increase in the number of layers and nodes enhances the network's accuracy. For example, if a network is designed to recognize images of birds, having more layers allows it to make finer distinctions, not just between a crow and a chicken, but also between a crow and an eagle.

Sukh Sohal, a senior consultant at Affinity Reply, indicates that deep learning models are very effective at identifying patterns, as they process information in layers. Each of these layers focuses on different aspects of the data, from simple features like the edges of an image to more complex structures, such as objects. This methodology enables the model to identify patterns in raw and unstructured data without the need for manual intervention, making it particularly useful in image recognition, voice processing, and natural language understanding.

Among the advantages of deep learning is its ability to uncover hidden insights and relationships within complex data that were not previously evident. Current models can analyze large volumes of information thanks to several features:

• Ability to process various types of data: These systems can handle both structured and unstructured data, analyzing social media posts, for example, to gain customer insights.
• Data scalability: Deep learning models tend to improve their performance as the volume of data increases, in contrast to some traditional algorithms that may plateau.
• Pattern discovery: They can analyze vast amounts of information to reveal complex patterns.
• Feature engineering: They save time as they do not require humans to extract features from the data.
• Efficiency: Once properly trained, they execute tasks much faster than a human.

The operation of a deep learning model involves multiple layers of interconnected nodes, where each layer refines its predictions and classifications. Each node takes data from the previous layer, processes it, and sends it to the next. This process, known as forward propagation, transforms the data through complex mathematical operations. Activation functions are applied to perform nonlinear transformations, generating a statistical model as a result.

However, before these models can be used, they must be trained to recognize patterns in the data. This is where the concept of backpropagation comes into play. When a prediction is made, the difference between the expected outcome and the actual value, known as the error, is calculated. This error is propagated backward through the network to adjust the weights and parameters of the model, thus improving its accuracy with each iteration.

Unlike traditional models that require human supervision for feature extraction, in deep learning, the system learns to define its own set of features through an unsupervised learning process. For example, a model is fed training data that includes images of cats and other animals, allowing it to define its own features and continue improving its identification capabilities.

The applications of deep learning are numerous and span tasks ranging from big data analytics to language translation, medical imaging, and stock price prediction. Digital assistants like Siri, Cortana, and Alexa are notable examples of this technology, enabling interactions based on natural language processing. Moreover, in sectors like manufacturing, it can monitor processes and detect anomalies, optimizing quality and operational efficiency.

In finance, it is used to develop trading strategies, predict stock prices, and detect fraud. It is also beginning to make a significant impact in the healthcare sector, where it can help predict diseases and develop better diagnoses and treatments. As new applications are discovered, deep learning continues to transform various industries, including agriculture, where it is utilized to identify diseases in crops and improve yields.