Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning (Coursera)
TensorFlow is a popular open-source framework for machine learning and probably the best tool you can use to implement machine learning and deep learning algorithms and principles. This TensorFlow course offered on Coursera is a part of TensorFlow in Practice Specialization by deeplearning.ai.
This course is suitable for software developers who have some experience in Python coding and some knowledge of machine learning and deep learning and who want to build scalable AI-powered algorithms in TensorFlow. It teaches how to use TensorFlow to implement the principles of machine learning and deep learning so learners can start building and applying scalable models to real-world problems.
The course comprises of 4 weekly modules that take learners from basic to mastery of TensorFlow. They cover the following topics:
- Introduction to what Machine Learning and Deep Learning are
- Introduction to Computer Vision
- Coding a Computer Vision Neural Network
- Introduction to Convolutional Neural Networks and Pooling
- Implementing convolutional layers and pooling layers
- Understanding ImageGenerator
- How to handle complex real-world images
There are abundant coding examples and programming assignments throughout the course. By the end of the course learners are able to gain practical skills to come up with scalable solutions to real-life AI challenges.
The course is taught by Lawrence Moroney, an AI advocate at Google. He has authored over 30 programming books and several science fiction novels.
Key Highlights
- Learn to apply TensorFlow skills to a wide range of problems and projects
- Learn the best practices for using TensorFlow
- Build a basic neural network in TensorFlow
- Understand how to use convolutions to improve your neural network
- Train a neural network for a computer vision application
Duration : 4 weeks, 6-9 hours per week
Sign up Here