Deep Learning and Neural Networks: From Theory to Applications is a course that provides a comprehensive overview of Deep Learning and Neural Networks. The course covers the basics of Deep Learning, Neural Networks, and their importance. It also delves into the history of Deep Learning, the different types of Neural Networks, building blocks of Neural Networks, and activation functions, loss functions, and optimizers.

The course further explores the fundamentals of Neural Networks such as the Perceptron and Multi-layer Perceptron, Gradient Descent, and Backpropagation, Regularization Techniques, Initialization Schemes, and Hyperparameter Tuning.

The course also covers advanced topics such as Convolutional Neural Networks, including their basics, popular architectures such as VGG, Inception, and ResNet, Transfer Learning, and Fine-tuning, and Data Augmentation Techniques. Additionally, Recurrent Neural Networks such as RNN Basics, LSTM and GRU Networks, Applications of RNNs (Sequence to Sequence, Language Translation, Speech Recognition), and Generative Models are also covered.

Finally, the course offers a project that enables students to apply the knowledge they have gained by implementing a Deep Learning project using the concepts learned in the course. The project will be presented and graded at the end of the course.