Introduction

According to IBM, “Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.” IBM. (2023). By using different types of data and using different learning strategies, deep learning differs from traditional machine learning.

Deep learning models are a significant advancement in AI technology, enabling efficient and efficient interpretation of data sets. They have helped us grow in various fields by automating them. However, challenges such as bias, privacy, and ethical issues pose threats to their future. To ensure a safer and technologically advanced future, it is crucial to make these models more effective and safer for research and real-world applications.

Structured, labeled data is used by machine learning algorithms to produce predictions, which means that the model's input data is used to identify certain features that are then arranged in tables. This doesn't necessarily imply that it doesn't employ unstructured data; rather, it just indicates that if it does, it typically goes through some pre-processing to put it in a structured manner. Some of the data pre-processing that is generally involved with machine learning is eliminated with deep learning. These algorithms can handle text and visual data that is unstructured and automate feature extraction, reducing the need for human specialists.

How does Deep learning differentiate from Machine Learning? To understand that IBM explains in their article “An introduction to deep learning”, “deep learning is a subdomain of machine learning. With accelerated computational power and large data sets, deep learning algorithms are able to self-learn hidden patterns within data to make predictions. In essence, you can think of deep learning as a branch of machine learning that's trained on large amounts of data and deals with many computational units working in tandem to perform predictions.” (Madan & Madhavan, 2020)

Now furthermore we compare the human brain and deep learning, since there are many similar terminologies that can be related to neurology. Since in the human brain, neurons are the building blocks, deep learning has similar aspects that allow modeling of nonlinear functions called perceptron. The perceptron receives a list of input signals and changes them into output signals just like neuron transmits electrical signals through the brain. The job of each layer of perceptrons is to interpret a particular pattern in the data. The architecture is known as neural networks (or artificial neural networks) because a network of these perceptrons resembles how neurons in the brain create a network.

This explains to us how complex and how similar the concept of deep learning is to us as humans. Now let’s discuss its practical uses.