Introducing you to the fundamentals of convolutional neural networks (CNNs) and computer vision. We will learn about what makes CNNs tick, discuss some effective techniques to improve their performance, and undertake a big practical project.
This course offers a deep dive into an advanced neural network construction – convolutional neural networks. First, we explain the concept of image kernels, and how it relates to CNNs. Then, you will get familiar with the CNN itself, its building blocks, and what makes this kind of network necessary for computer vision. You’ll apply the theoretical bit to the MNIST example using TensorFlow, and understand how to track and visualize useful metrics using TensorBoard in a dedicated practical section. Later in the course, you’ll be introduced to a handful of techniques to improve the performance of neural networks, and a huge real-world practical project for classifying fashion item pictures. Finally, we will cap it all off with an intriguing look through the history of the most influential CNN architectures.
Introducing you to the workings of convolutional neural networks (CNNs) and computer vision. You will learn the basics of convolution and its role in CNNs, as well as the main structure of such networks and how to implement them in practice.
Student feedback
“This course is a fantastic training opportunity to help you gain insights into the rapidly expanding field of Deep Learning and Computer Vision through the use of Convolutional Neural Networks. By the end of this course, you will be completely equipped with all the tools you need to confidently work on CNN projects!”
Silver medal at Physics Olympiad
Convolutional Neural Networks with TensorFlow in Python
with Nikola Pulev and Iskren Vankov
