Tensorflow 2.0 Practical

AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.

The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:

1. Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions
2. Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.
3. Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.
4. Develop AI models to perform sentiment analysis and analyze customer reviews.
5. Perform AI models visualization and assess their performance using Tensorboard
6. Deploy AI models in practice using Tensorflow 2.0 Serving

The course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techniques and can directly apply these skills to solve real world challenging problems using Google’s New TensorFlow 2.0.