Introduction DNNs, which stand for Deep Neural Networks, are the driving force behind state-of-the-art Artificial Intelligence and Deep Learning. By stacking several layers on top of each other, i.e., one working as a filter or a feature extractor, they can learn complex patterns from information such as images, voice, and text. In contrast to simple neural networks, DNNs extract representation hierarchies, allowing them to represent nuanced differences and abstract ideas. This layered depth provides them with better performance in applications such as self-driving, medical diagnosis, natural language processing, and more. In this blog, you will learn: What constitutes a neural network as “deep” Standard architectures include deep feedforward, CNN, RNN, Transformer, and Autoencoders. Key training methods may include optimization, regularization, and hyperparameter tuning. Real-world examples of impactful transformative effects. How to implement and train your own DNN best practic...