Deep learning is one of the most recent and advanced topics in machine learning, with several applications in many fields. It shows promising results in many areas, from computer vision to drug discovery and stock market prediction. There are many books and articles on deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potential in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning and then has focused on Generative Adversarial networks and their applications with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms (especially GAN algorithms) using python, TensorFlow, and Keras.

The course has seven chapters and starts with some fundamentals in machine learning concepts and end with some idea in CycleGAN. Each chapter has some quizzes and assignments to test students learning. It also provides the solution for each project.

I would expect this course’s contents to be welcomed worldwide by undergraduate and graduate students and researchers in deep learning, including practitioners in academia and industry.