Recent advances in deep learning made neural networks the algorithm of choice to solve problems in various real-world domains, such as computer vision and natural language processing. Inspired by the structure of the brain, artificial neural networks are very versatile, as they can learn any arbitrarily complex function. Neural networks now increasingly outperform humans on various tasks such as recognizing faces, or beating the world’s best players in games like chess and Go.
Upon completion of this course, you will understand the foundations of artificial neural networks, their structure, and how to train a model. You will become familiar with the different layers of the network and learn how to minimize the cost function using gradient descent and backpropagation. During the training, you will gain hands-on experience by implementing your own neural network from scratch using PyTorch, which will strengthen your understanding of the underlying concepts.