Deep Learning in Practice I: Tensorflow Basics and Datasets

You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory. You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks. Do you want an automated process for developing deep learning solutions.

This course is then designed for you. Welcome to Deep Learning in Practice, with NO PAIN. This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namelyDeep Learning in Practice I: Tensorflow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects.

Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNN algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition.

The student will learn how to build a face recognition app in Tensorflow and Keras. Deep Learning in Practice I: Basics and Dataset DesignThere are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.