# Review of Deep Learning

arXiv: https://arxiv.org/abs/1804.01653 · HTML: https://mlsanity.com/paper/1804.01653/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [Stochastic Physics-Informed Neural Ordinary Differential Equations](https://mlsanity.com/paper/2109.01621/) (2021)
- [A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network](https://mlsanity.com/paper/2202.10454/) (2022)
- [PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics](https://mlsanity.com/paper/2502.19037/) (2025)
- [BugNIST -- a Large Volumetric Dataset for Object Detection under Domain Shift](https://mlsanity.com/paper/2304.01838/) (2023)
- [A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks](https://mlsanity.com/paper/2212.13904/) (2022)

## Abstract (arXiv)

In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.