In recent few years, deep learning has been successfully applied in many fields to optimize decision making including self-driving cars, health care, machine translation, image recognition, etc. In wireless communication, deep learning has been used in channel estimation, signal classification, massive MIMOs, heterogeneous networks, energy harvesting, device-to-device (D2D) communications, and so on. In this paper, we applied machine learning (ML) and deep learning (DL) neural networks to RF signal recognition. Specifically, we built, trained, and tested two ML models, SVM and XGBoost, and two DL models, ConvNet and ResNet. We utilized the online dataset at radioml.com. Our goals are to learn how to scientifically apply ML/DL in terms of dataset processing, deep neural network constructing, training, testing, fine-tuning, and results analyzing and reporting.