This chapter introduces deep learning and the role of GPU acceleration. It covers key architectures such as deep neural networks (DNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The chapter examines the mathematical representation of these networks. It addresses the challenges of RNNs, including the vanishing gradient problem and the function of long short-term memory (LSTM) units. This chapter discusses the significance of GPU acceleration in deep learning, highlighting various GPU-based optimization techniques. It also explores practical aspects of training deep learning models, such as data preprocessing and tensor manipulation. Finally, the concepts presented are applied to the task of image classification using the MNIST dataset.