This paper provides a comprehensive survey of deep learning (DL) approaches, starting from the inception of Deep Neural Networks (DNNs) and covering various advanced techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Auto-Encoders (AEs), Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (DRL). The survey highlights the advancements in DL, including state-of-the-art performance in image processing, computer vision, speech recognition, machine translation, medical imaging, robotics, and natural language processing. It discusses the different types of learning methods, such as supervised, semi-supervised, and unsupervised learning, and their applications. The paper also covers recent developments in DL, including advanced variants of these approaches and the use of frameworks, SDKs, and benchmark datasets for implementation and evaluation. Additionally, it addresses the challenges of DL, such as big data analytics, generative models, and energy efficiency, and presents solutions to these challenges. The survey concludes with a discussion on the future directions and potential applications of DL.This paper provides a comprehensive survey of deep learning (DL) approaches, starting from the inception of Deep Neural Networks (DNNs) and covering various advanced techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Auto-Encoders (AEs), Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (DRL). The survey highlights the advancements in DL, including state-of-the-art performance in image processing, computer vision, speech recognition, machine translation, medical imaging, robotics, and natural language processing. It discusses the different types of learning methods, such as supervised, semi-supervised, and unsupervised learning, and their applications. The paper also covers recent developments in DL, including advanced variants of these approaches and the use of frameworks, SDKs, and benchmark datasets for implementation and evaluation. Additionally, it addresses the challenges of DL, such as big data analytics, generative models, and energy efficiency, and presents solutions to these challenges. The survey concludes with a discussion on the future directions and potential applications of DL.