Several studies concentrate on two-dimensional convolutional neural networks. They are especially useful in image recognition problems. 1D CNNs are covered in some detail, for example, in natural language processing (NLP). Few articles, however, provide an explanatory walkthrough on how to build a 1D-CNN for other machine-learning problems that you may be facing. This article attempts to bridge that chasm. When to Apply a 1D CNN? A CNN is effective at identifying simple patterns in data, which are then used to form more complex patterns in higher layers. When you expect to derive interesting features from shorter (fixed-length) segments of the overall data set and the location of the feature within the segment is not important, a 1D CNN is very effective. This is applicable to the analysis of sensor data time sequences (such as gyroscope or accelerometer data). It also applies to the analysis of any type of signal data over a predetermined time period (such as audio signals). Another