At the same time, NNs are usually criticized for being “black-box” models that are hard to interpret and understand, with an aura of mystery surrounding these algorithms. In this short course, we will delve into the foundations of neural networks, emphasizing approaches and best practices to model training, independent validation and testing, as well as model deployment. We will describe both the basic concepts and building blocks of the neural network architectures, and also touch upon the more advanced models. Our objective is to explain how neural network models can be understood in comprehensive but relatable terms for participants coming from a broad range of backgrounds.
At the same time, NNs are usually criticized for being “black-box” models that are hard to interpret and understand, with an aura of mystery surrounding these algorithms. In this short course, we will delve into the foundations of neural networks, emphasizing approaches and best practices to model training, independent validation and testing, as well as model deployment. We will describe both the basic concepts and building blocks of the neural network architectures, and also touch upon the more advanced models. Our objective is to explain how neural network models can be understood in comprehensive but relatable terms for participants coming from a broad range of backgrounds.