Deep Learning: Convolutional Neural Networks with Tensorflow 2

In this self-paced course, you will learn how to use Tensorflow 2 to build convolutional neural networks (CNNs). We'll take an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. We'll apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. We'll learn about techniques that help to improve performance, such as batch normalization, data augmentation, and transfer learning. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.

This course is intended for:

• Anyone interested in deep learning and machine learning
• Anyone who wants to implement convolutional neural networks in Tensorflow 2

Suggested prerequisites:

• Decent Python programming skill
• Know how to build a feedforward ANN in Tensorflow 2
• Experience with data science libraries like Numpy and Matplotlib

In this course, we will cover:

• what convolution is
• how to integrate convolution into neural networks
• best practices for designing CNN architecures
• how to apply CNNs to several image recognition datasets, both small and large
• batch normalization
• data augmentation
• transfer learning
• how to apply CNNs to natural language processing (NLP)