The Impact and Evolution of Deep Learning in Contemporary Real-World Predictive Applications

Abstract

Deep learning (DL) is making a significant impact on the lives of human beings, either directly or indirectly; we benefit from Artificial Intelligence (AI), Machine Learning (ML), DL and other technologies/networks in our day-to-day lives. DL not only can mimic a human brain and carry out tasks like a human being but has also outworked the approaches of ML, making itself the most efficient technology used nowadays. Maybe this is the reason why various fields like Cybersecurity, Medical Treatments, Traffic Control, Weather Forecast, Bioinformatics, Fraud Detection, Robotics, Vocal AI, Computer Vision, Autonomous Vehicles, E-Commerce and so on are using DL techniques/algorithms rather than the traditional ML approach. This review will illuminate the History of DL, i.e. what DL is, how and why it has evolved so far, and what the various parts of DL are. Though DL is considered the most efficient, it has some limitations, and we will investigate them gradually. We will also study all pre-researched DL Techniques and their implications for contemporary real-world predictive applications.