Deep Learning is a subset of Machine Learning and this field has some amazing
real-world applications such as Apple’s Siri to Google’s Self Driving Car.
Deep learning allows machines to solve relatively complex problems even when using
data that is diverse, less structured or interdependent. Deep learning is a form of machine
learning that is inspired and modeled on how the human brain works.
In this course students are introduced to the architecture of deep neural networks,
algorithms that are developed to extract high-level feature representations of data.
In addition to theoretical foundations of neural networks, including backpropagation and stochastic
gradient descent, students get hands-on experience building deep neural network models with Python
and learn how to use application program interfaces (APIs),
such as TensorFlow and Keras, for building a variety of deep neural networks:
convolutional neural network (CNN), recurrent neural network (RNN), self-organizing maps (SOM),
generative adversarial network (GANs), and long short-term memory (LSTM).
