Artificial Neural Networks and Deep Learning are the most recent and advanced topics in machine learning, with several applications in many fields. They show promising results in many areas, from computer vision to drug discovery and stock market prediction. 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 Artificial Neural Networks and Deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning 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 using Python, TensorFlow, and Keras.

The course provides some references and links for more reading. You have access to the Q/A session for asking your question. You also have access to communicate with the professor by the messaging system to ask your questions.

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.