Deep Learning in Practice II: Transfer Learning Projects

Develop commercial-level deep learning applications with state-of-the-art neural networks in Tensorflow 2 and Keras

Created by: Anis Koubaa
Professor of Computer Science

What Will I Learn?

• Develop complex deep learning projects using Transfer Learning
• Efficiently organize and structure deep learning projects
• Develop reusable libraries to reduce development time of deep learning projects
• Understand how to perform efficient training of classification projects with Transfer Learning
• Compare between deep learning models
• Learn State of the Art Classification Models
• Conduct training on local machine and Google Colab

Requirements

• Understand the basic concepts of machine learning (recommended, but not required)
• Be familiar with Python programming language and data structures (Numpy, Pandas)
• Understand the basic concepts of neural networks (recommended)

Target audience

• Someone who learned the concepts of deep learning, but want to master the practical aspects of deep learning projects
• PhD and Master students doing thesis on deep learning
• Any enthusiast about artificial intelligence and deep learning
• Computer vision practitioners
• Anyone who would like to learn about best practices in deep learning
• Anyone who like to quickly start with deep learning without having a background in it