This technical report provides an overview of deep learning in neural networks, highlighting its historical development and key concepts. It discusses the role of credit assignment paths (CAPs) in determining the depth of learning, and explores various learning paradigms including supervised, unsupervised, and reinforcement learning. The report traces the evolution of deep learning from early neural network models to modern applications, emphasizing the importance of hierarchical representations, efficient gradient descent methods, and the use of GPUs for accelerating deep learning. It also covers the development of backpropagation, the challenges of training deep networks, and the impact of unsupervised learning in facilitating supervised learning. The report highlights key milestones in deep learning, including the development of convolutional neural networks, recurrent neural networks, and deep belief networks. It also discusses the role of dynamic programming, regularization, and compression in deep learning, as well as the potential of deep learning in reinforcement learning and other domains. The report concludes with a discussion of current trends and future directions in deep learning.This technical report provides an overview of deep learning in neural networks, highlighting its historical development and key concepts. It discusses the role of credit assignment paths (CAPs) in determining the depth of learning, and explores various learning paradigms including supervised, unsupervised, and reinforcement learning. The report traces the evolution of deep learning from early neural network models to modern applications, emphasizing the importance of hierarchical representations, efficient gradient descent methods, and the use of GPUs for accelerating deep learning. It also covers the development of backpropagation, the challenges of training deep networks, and the impact of unsupervised learning in facilitating supervised learning. The report highlights key milestones in deep learning, including the development of convolutional neural networks, recurrent neural networks, and deep belief networks. It also discusses the role of dynamic programming, regularization, and compression in deep learning, as well as the potential of deep learning in reinforcement learning and other domains. The report concludes with a discussion of current trends and future directions in deep learning.