Summary of Week 6 -- Capsule 2 -- Practical Overview of RNNs

00:00:00 - 00:25:00

This video provides a brief overview of recurrent neural networks, including how they are composed of simple neurons and how the network can remember information over time. The video also discusses some of the problems that can occur when training RNNs, and demonstrates how to build a bi-directional RNN.

• 00:00:00 This video provides a brief overview of recurrent neural networks, their benefits, and limitations. The video explains how a recurrent neural network works to model sequential data, and how it updates its memory according to past experiences.
• 00:05:00 The video provides an overview of recurrent neural networks, including the use of parameters to specify how the network should transform inputs. It also introduces a more complicated parameterization, an LSTM.
• 00:10:00 This video introduces the basics of recurrent neural networks, including a description of the error function and how it is used to calculate the gradient of the error. It also covers the use of the back propagation algorithm to train the network.
• 00:15:00 The video discusses the practical limitations of gradient descent for recurrent neural networks, which include long-term dependencies. The video then goes on to explain why gradient descent can be difficult to learn in such cases.
• 00:20:00 In this video, a graduate student discusses the problems that can occur when training a neural network, and how to solve them. One solution is to use gradient clipping, while another is to adjust the weights in order to prevent gradient explosion.
• 00:25:00 This video covers the basics of recurrent neural networks (RNNs), including how they are composed of simple neurons and how the network can remember information over time. The video also discusses some of the problems that can occur when training RNNs, and demonstrates how to build a bi-directional RNN.