Framing Artificial Intelligence Deep Learning

Oct 1 / Christina Tziouvara

Programmers of artificial intelligence deep learning and other technologies make efforts to deploy these advancements for the benefit of the human life. Deep learning is an invaluable asset for data science, especially in terms of data structure provision.

Artificial Intelligence Deep Learning: Explained and Differentiated from Machine Learning

In short, artificial intelligence is the technology that simulates the processes of human intelligence. And, machine learning is an AI application that has the ability to identify patterns among provided data. In addition, the computations made, due to continuous data provision, can lead machine learning to produce results that rely on previous data analysis. Deep learning is a subset of machine learning; and, they both are applications of the wider set of AI technology.

As deep learning, we define the formation of algorithmic processes that encompasses predictive and statistical models. In addition, the overall goal of this technique is to facilitate procedures of data elaboration through pattern recognition, in short periods of time and with easiness.

Even though, machine learning and deep learning are similar techniques; however, there are points that differentiate the two concepts, when implemented:

  • The deep learning algorithms are more complicated, compared to machine learning; and, the level of complexity keeps escalating.
  • Machine learning is a procedure that demands accuracy and supervision (feature extraction process). On the other hand, deep learning is an autonomous procedure, with faster functioning rate and more accurate performance results.

A Glance to the Processes of this Term

As an AI application, deep learning simulates human processes. In fact, the entire process builds up through data provision, also known as training data. Namely, training data and pattern “observation”, are the two mediums that set up deep learning, in order to proceed to a predictive model. Every time the process occurs, the predictive model gets enriched; hence, the process doesn’t cease to improve. Furthermore, the fact that the predictive model enhances, in each application, is interpreted in results with greater precision, by amplifying the level of complexity of the statistical models.

Current Applications of Deep Learning

Translation, virtual assistance, facial recognition are only a few examples, where deep learning applies. This technique is part of our reality and many sectors have integrated them, in order to facilitate their activities, and in some cases transform the field’s landscape. Here are a few examples:

  • Finance: The field of finance deploys such procedures to assess operations that involve investments; approving loans; fraudulent activity etc.
  • Healthcare industry: Medicine deploys this technology, in terms of disease pattern recognition; imaging solutions etc.
  • Sectors with customer service activity: For improved customer experience purposes, businesses encompass deep learning processes in the form of chatbots.

Admittedly, the contribution of artificial intelligence deep learning is significant, especially for data scientists; however, there are limits to its processes. Even though the idea behind this technological term is reaching performance levels of human intelligence, there are limitations to its applications. In this technique, precision equals more parameters; hence, provision of large amounts of data is imperative. Moreover, deep learning training corresponds to solution provision on a specific task. Therefore, fulfilling needs of another problem, demands another deep learning training.

Our team doesn’t cease to observe the emerging entrepreneurial demands. In fact, our platform is continuously updated with accurate information provision, and high-quality upskilling courses, in a vast range of subjects. Keep track of iED Academy’s news.