Deep Learning is now of the hottest trends in Artificial Intelligence and Machine Learning with daily reports of practical amazing new achievements. It is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on smartphones, and much more. Most popular deep learning libraries are developed in Python language and supported by big companies like Google and Facebook. From these libraries, TensorFlow and Keras have been widely adopted in research and production.

This tutorial will aim to provide understanding of developing practical applications with state-of-the-art deep learning software tools (Keras/TensorFlow). Several Functional examples will be presented in Both frameworks using Jupyter notebooks and Python ecosystems. Prerequisites: Bring your own laptop. Solid understanding of the fundamentals of linear algebra. Programming skills (Python Language) . Basic understanding of machine learning methods and deep neural networks. Familiarity with basic linear algebra.