This review discusses the application of deep learning in computational biology, focusing on regulatory genomics and biological image analysis. Deep learning, a subset of machine learning, leverages large datasets to uncover hidden structures and make accurate predictions. In regulatory genomics, deep neural networks can predict molecular traits from DNA sequences, capturing nonlinear dependencies and interaction effects. Convolutional neural networks (CNNs) are particularly effective for this task, allowing direct training on raw DNA sequences without predefined features. In biological image analysis, deep networks have achieved significant success in tasks such as pixel-level classification, whole-cell classification, and tissue analysis. Pre-trained models from other domains can be fine-tuned for specific tasks, and visualization techniques help interpret the learned features. The review also covers practical considerations, including data preparation, model architecture selection, and training techniques, providing a comprehensive guide for computational biologists interested in applying deep learning to their research.This review discusses the application of deep learning in computational biology, focusing on regulatory genomics and biological image analysis. Deep learning, a subset of machine learning, leverages large datasets to uncover hidden structures and make accurate predictions. In regulatory genomics, deep neural networks can predict molecular traits from DNA sequences, capturing nonlinear dependencies and interaction effects. Convolutional neural networks (CNNs) are particularly effective for this task, allowing direct training on raw DNA sequences without predefined features. In biological image analysis, deep networks have achieved significant success in tasks such as pixel-level classification, whole-cell classification, and tissue analysis. Pre-trained models from other domains can be fine-tuned for specific tasks, and visualization techniques help interpret the learned features. The review also covers practical considerations, including data preparation, model architecture selection, and training techniques, providing a comprehensive guide for computational biologists interested in applying deep learning to their research.