• 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 NOPAIN!

This course is the first course on a series of Deep Learning inPractice Courses of Anis Koubaa, namely

• Deep 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 CNNalgorithms. 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 Design

There 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.

This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.

I have seen several people developing deep learning projects, but they fail to make their projects organized and reusable for other projects. This would lead to losing huge time when switching from one project to the others. In this course, I present several tips to efficiently structure deep learning projects that make you generate results in one simple click, instead of losing time into manual processing data collected from deep learning models.

The hands-on projects explain in detail the whole loop of deep learning projects starting from data collection, to data loading, pre-processing, training, and evaluation.

By the end of the course, you will be able to design deep learning projects in very little time with a comprehensive set of results and visualizations.