Why is Deep Learning the Future of Machine Learning?

Deep learning involves a type of algorithm known as a neural network, a specialized category within the broader field of machine learning. These networks aim to replicate the functioning of the human cortex by organizing themselves into layers of interconnected nodes, analogous to how the human brain consists of neurons. Deep learning methods strive to arrive at human-like insights by perpetually scrutinizing data within a specific logical framework. These algorithms construct a computational model featuring multiple layers of data processing.

Although the first neural networks were proposed in the middle of the twentieth century, they were not popular until this century because the traditional form of machine learning and AI outperformed the first neural network models. With the introduction of modern deep learning algorithms, they have become one of the most widely used learning algorithms. The significant difference between the traditional neural network and the current deep-learning model was the use of many successive hidden layers in the network. In fact, the term "Deep" in the Deep Neural Network refers to the number of layers that make multiple levels of abstraction to represent the data. This number of layers is always more than three layers.  

Deep learning networks learn by discovering intricate structures in the data that they experience. The advantages that deep learning models provide are that they can learn a compact and valuable representation of data that is useful for different machine learning tasks. Moreover, deep learning algorithms removed the need for human experts for feature engineering, which was an essential part of many traditional learning algorithms. 

Deep Learning in Handling Large-Scale Data

Deep learning has attracted much attention because it is particularly suitable for supervised learning, which is potentially useful for most real-world applications. In fact, deep learning is a groundbreaking tool for processing large volumes of data since the deep-learning algorithm's performance increases as it experiences more data.

As the amount of data increases, the machine becomes more skilled at recognizing sophisticated hidden patterns among the data. These properties lead deep learning techniques to become instrumental not only in many everyday applications in image processing, language processing, etc. but also in the discovery of novel drugs and the detection of diseases and even subatomic particles. For more valuable insights on the importance of deep learning, be sure to be a part of our Fundamentals of Deep Learning Online Training.