Watch the following videos to get an idea of what we're doing: <iframe width="560" height="315" src="https://www.youtube.com/embed/aircAruvnKk?si=Fh6BzAWlTo_ieTjg" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> <iframe width="560" height="315" src="https://www.youtube.com/embed/TkwXa7Cvfr8?si=_RYNX0vUgGD2tX6w" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> <iframe width="560" height="315" src="https://www.youtube.com/embed/IHZwWFHWa-w?si=5Jw_MeXWiMAXuRKY" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> <iframe width="560" height="315" src="https://www.youtube.com/embed/zxQyTK8quyY?si=8m3S0MYl8FlgMYR8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> This can help give you an overview of what we're trying to do when we use Neural Networks. There are a number of ways to configure individual Neurons, how to handle Back-propagation, and what Hyperparameters to use. There are many kinds of Neural Networks, review these videos and run the example code / datasets to solidify your understanding. [[What is a Neuron?]] [[Recurrent Neural Networks (RNNs)]] [[Vanishing Gradient Problem]] [[Long Short-Term Memory Networks (LSTMs)]] [[Gated Recurrent Units (GRUs)]] [[RNNs with Text]] [[When to use RNNs, LSTMs, GRUs]]