# Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners
## 3 Deep Learning Fundamentals and Elements  
Artificial Intelligence (AI) could be defined as the capacity of computers to exhibit or simulate intelligent behaviour when automating tasks that are normally performed by humans [20; 46]. AI was born in the 1950s, when researchers in the field of computer science started asking whether computers could be made to think [47]. In the beginning, researchers believed that AI could be achieved with handcrafted programs with a large set of explicit predefined coding rules specified by human operators [20; 46]. This kind of AI is known as symbolic, and it was the dominant paradigm in AI from the 1950s until the late 1980s [47]. Even though this paradigm is useful at solving well-defined logical problems like playing chess, it is incapable of solving complex problems where finding out explicit rules is very hard, for instance, image classification, object detection, speech recognition, or language translation. However, ML emerged as a new approach replacing symbolic AI [46; 47] and it started to grow quickly in the 1990s, until becoming probably the most popular and most successful field of AI. Broadly speaking, ML can be seen as the ability of computers to extract information from the input of raw data and to learn from experience, thus generating complex inferences based on the relationships within the data [48]. Thereby, ML builds predictive models without using predefined coding rules [20], and it is able to deal with large and complex datasets for which statistical analysis would be unfeasible. We can divide ML algorithms into three categories, namely, supervised, unsupervised, and reinforcement learning. In supervised learning, the goal is to learn a function that, from a sample of input-output pairs (labeled as training data), approximates the relationship among inputs and outputs well enough in the labeled training data, and then the inferred function can be used for mapping new data. By contrast, unsupervised learning seeks to identify hidden patterns present in datasets, without the requirement that they are labeled. Lastly, reinforcement learning aims to maximize the accuracy of algorithms learning actions that maximize some reward [49]. ML algorithms strongly depend on _feature engineering_, which is the process of using domain knowledge of the data to create features that make these algorithms work. The process requires lots of time and effort for feature selection, and features must extract relevant information from huge and diverse data in order to produce the best outcome [48; 50]. These approaches have shown good results on structured data, such as sales predictions or recommendation systems; however, in unstructured domains, such as computer vision and natural language processing, feature engineering is a challenging task for ML algorithms [47]. To handle these problems, a special branch of ML arose, so-called DL, which is based on the concept of Artificial Neural Networks (ANNs, also known simply as Neural Networks) [51]. The major difference between DL and traditional ANNs are the number of hidden layers [48]. ANNs are usually limited to three layers and they are known today as Shallow Neural Networks, whereas DL are NNs with many layers and they are referred to as Deep Neural Networks (DNNs) [52]. DNNs use multiple layers to explore more complex nonlinear, patterns and learn meaningful relationships within the data [51], and they learn and construct inherent features from each successive hidden layer of neurons, by minimizing or even removing the need for feature engineering. This last factor resulted in DL often outperforming ML techniques [50], revolutionizing this field with outstanding results and robustness to input noise and variability in diverse tasks [53]. Therefore, some authors point that DL could be seen as a specific case of BD solution, since the implementation of BD techniques, such as the parallelization in GPUs, has made this computationally demanding solution viable.