Sometimes this approach is called semi-supervised learning as well. Here is an example of RL: If environment samples inputs: x t ~ ρ, agent predict: y ^ t = f ( x t ), agent receive cost: c t ~ P ( c t | x t, y ^ t ) where P is an unknown probability distribution, the environment asks an agent a question, and gives a noisy score as the answer. From then on, several advanced methods have been proposed based on RL. Type of Deep Learning Approachesĭeep Reinforcement Learning is a learning technique for use in unknown environments. In some articles, DL has been described as a universal learning approach that is able to solve almost all kinds of problems in different application domains. Recent literature states that DL based representation learning involves a hierarchy of features or concepts, where the high-level concepts can be defined from the low-level ones and low-level concepts can be defined from high-level ones. Learning methods based on representations of data can also be defined as representation learning. DL, on the other hand, consists of several layers in between the input and output layer which allows for many stages of non-linear information processing units with hierarchical architectures to be present that are exploited for feature learning and pattern classification. For example, in Artificial Neural Networks (ANN), the parameters are the weight matrices. Learning is a procedure consisting of estimating the model parameters so that the learned model (algorithm) can perform a specific task. DL which uses either deep architectures of learning or hierarchical learning approaches), is a class of ML developed largely from 2006 onward. Since its inception DL has been creating ever larger disruptions, showing outstanding success in almost every application domain. Neural Networks (NN) is a subfield of ML, and it was this subfield that spawned Deep Learning (DL). Since the 1950s, a small subset of Artificial Intelligence (AI), often called Machine Learning (ML), has revolutionized several fields in the last few decades. However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches.
Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey.
This work considers most of the papers published after 2012 from when the history of deep learning began. Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. In recent years, deep learning has garnered tremendous success in a variety of application domains.
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