Deep Learning in Practice I: Tensorflow Basics and Datasets
Pain-Free Deep Learning Projects and Dataset Design in Tensorflow 2.0.
What you’ll learn
- Develop complex deep learning projects
- 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
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, but not required)
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 first course on a series of Deep Learning in Practice 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 (to release on August 2020): 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 (to release on September 2020): 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.
Who this course is for:
- Someone who learned the concepts of deep learning, but want to master the practical aspects of deep learning projects
- Ph.D. and Master students doing a thesis on deep learning