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

What you'll 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)

Description

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 second course on a series of Deep Learning in Practice Courses of Anis Koubaa, namely

Deep Learning in Practice I: Basics and Dataset Design: 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: Deployment of Deep Learning Models: the student will learn how to deploy deep learning models in a production environment. We will present the deployment techniques used in industry such as Flask, Docker, Tensorflow Serving, Tensorflow JavaScript, and Tensorflow Lite, for deployment in a different environment. Despite important, this topic has little coverage in tutorials and documentations.

Deep Learning in Practice II: Transfer Learning Projects

This course introduces you to transfer learning and demonstrate to you how to use transfer learning in real-world projects.

In this course, I demonstrate how to conduct training of a deep learning classification model using transfer learning.

Besides, you will learn how to evaluate the performance of a model with some pre-configured libraries that makes it easy to obtain the results and interpret them.

I also provide ready-to-use Google Colab Notebooks with all codes used in this course.

The same code can be easily adapted and reused for any classification project in an automated way.

Who this course is for:

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