๐๏ธ Week 11 - Neural networks and deep learning
This week, we will explore neural networks and deep learning, two powerful techniques for modeling complex data. We will start by discussing the basic building blocks of neural networks, including neurons, activation functions, and layers. We then move to analyze deep learning via a probabilistic perspective, where we will discuss cast many of the traditional tricks and techniques used in deep learning as special cases of probabilistic models. We will also cover the concept of overfitting and regularization, and how to optimize neural networks using stochastic gradient descent and backpropagation. Then, we will discuss Bayesian neural networks, which provide a probabilistic framework for modeling uncertainty in neural networks. Finally, we will uncover powerful connections between neural networks and Gaussian processes.