This survey provides an overview of deep learning (DL) approaches, starting from the Deep Neural Network (DNN) and covering Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Auto-Encoders (AE), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), and Deep Reinforcement Learning (DRL). It discusses various DL techniques, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning. The survey also includes recent developments in DL, such as advanced variants of these techniques. It covers DL applications in image processing, computer vision, speech recognition, machine translation, medical imaging, robotics, and other domains. The survey also includes frameworks, SDKs, and benchmark datasets used for implementing and evaluating DL approaches. It highlights the state-of-the-art performance of DL in various tasks, such as image classification on the ImageNet dataset and automatic speech recognition on the TIMIT dataset. The survey discusses the scalability of DL, the universal learning capability of DL, and the robustness of DL approaches. It also addresses challenges in DL, including big data analytics, scalability, data generation, energy efficiency, and multi-task learning. The survey provides an in-depth analysis of various DL architectures, including DNNs, CNNs, RNNs, AEs, RBMs, GANs, and DRL. It also discusses the history of DL, the gradient descent algorithm, momentum, learning rate, and weight decay. The survey concludes with a discussion of the future trends and applications of DL.This survey provides an overview of deep learning (DL) approaches, starting from the Deep Neural Network (DNN) and covering Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Auto-Encoders (AE), Deep Belief Networks (DBN), Generative Adversarial Networks (GAN), and Deep Reinforcement Learning (DRL). It discusses various DL techniques, including supervised, semi-supervised, and unsupervised learning, as well as reinforcement learning. The survey also includes recent developments in DL, such as advanced variants of these techniques. It covers DL applications in image processing, computer vision, speech recognition, machine translation, medical imaging, robotics, and other domains. The survey also includes frameworks, SDKs, and benchmark datasets used for implementing and evaluating DL approaches. It highlights the state-of-the-art performance of DL in various tasks, such as image classification on the ImageNet dataset and automatic speech recognition on the TIMIT dataset. The survey discusses the scalability of DL, the universal learning capability of DL, and the robustness of DL approaches. It also addresses challenges in DL, including big data analytics, scalability, data generation, energy efficiency, and multi-task learning. The survey provides an in-depth analysis of various DL architectures, including DNNs, CNNs, RNNs, AEs, RBMs, GANs, and DRL. It also discusses the history of DL, the gradient descent algorithm, momentum, learning rate, and weight decay. The survey concludes with a discussion of the future trends and applications of DL.