Here, we introduce the concept of image kernels and transformations. We will explain how they work through convolution, which forms the basis for CNNs.

This is where we have an in-depth discussion of the Convolutional Neural Networks: you will understand the motivation and fundamental strength of this type of network and learn more about the concepts and layers that make it work – feature maps and pooling layers. Finally, you will discover how the dimensions change in such a network.

In this section, we quickly revise the main concepts of neural networks in general. These include activation functions, overfitting and early stopping, and optimizers. This part is intended only as a reference and is not a substitute for a full course on the basics of Machine Learning.

In this part of the course, you will learn how to set up Python 3 and load up Jupyter. We’ll also show you what the Anaconda Prompt is and how you can use it to download and import new modules.

In this section, you will put theory into practice. You will develop a simple CNN architecture, implement it from scratch, and train the model on the MNIST example.

After training a model to classify digits, we turn our attention to tracking and visualizing different metrics. The ability to analyze your models is crucial in Machine Learning, and here, we will lay the groundwork which makes that possible through TensorBoard.

This section introduces 3 crucial concepts for improving the performance of neural networks in general. We will discuss theoretically L2 regularization and weight decay, Dropout, and Data Augmentation, which you will practice in the next section.

So far, you have already gained some practice with CNNs in a somewhat sterile environment. However, the real world is much messier than the MNIST example. That’s why, in this section, we have prepared a huge practical example inspired by tasks you are very likely to do in your job. You will practice classifying clothes and other fashion items with multiple labels. Here, you will see why the neat results from the MNIST example are almost unattainable and gain valuable insight into the procedure of undertaking such a project.

In this section, we briefly discuss what aspects of neural networks you need to be careful with, illustrating them with real examples of models that exhibit unexpected behavior.

This section is dedicated to exploring the most influential and breakthrough Convolutional Neural Networks of the past - AlexNet, VGG, GoogLeNet, and ResNet - and explaining the concepts behind their architectures. We will also look at the Computer Vision competition ILSVRC and its development over the years.