Keras Deep Learning and Generative Adversarial Networks (GAN) [Video]

The course begins with the fundamentals of Python, encompassing concepts such as assignment, flow control, lists, tuples, dictionaries, and functions. We then move on to the Python NumPy library, which supports large arrays and matrices. Before embarking on the journey of deep learning, a comprehensive theoretical session awaits, expounding upon the essential structure of an artificial neuron and its amalgamation to form an artificial neural network. The exploration then delves into the realm of CNNs, text-based models, binary and multi-class classification, and the intricate world of image processing. The transformation continues with an in-depth exploration of the GAN paradigm, spanning from fundamental principles to advanced strategies. Attendees will have the opportunity to construct models, harness transfer learning techniques, and venture into the realm of conditional GANs. Once we complete the fully connected GAN, we will then proceed with a more advanced Deep Convoluted GAN, or DCGAN. We will discuss what a DCGAN is and see the difference between a DCGAN and a fully connected GAN. Then we will try to implement the DCGAN. We will define the Generator function and define the Discriminator function. By the end of the course, you will wield the skills to create, fine-tune, and deploy cutting-edge AI solutions, setting you apart in this evolving landscape.