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.