In this course you will embark on a comprehensive journey through the fundamentals of Artificial Intelligence. The course starts with the theory behind deep neural networks and their various architectures. Then we get into gradient descent, back-propagation, activation functions, and the fundamentals of training high-impact models so as to reduce bias and variance. Next, you will then build and fine-tune deep Neural Networks, and apply the learned theories including techniques such as hyperparameter tuning, optimization, and regularization. We will cover architectures and concepts such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), attention mechanisms, transformers, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Natural Language Processing (NLP), computer vision, and more!