Neural network architectures
The purpose of this chapter is to introduce you to the various types of traditional deep learning, including CNNs, RNNs, and LSTMs, along with their histories, key concepts, and applications.
In this course, we will focus on different classical neural network architectures, including artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
we will use real-world business examples to illustrate the concepts and techniques covered in the lectures. By the end of the course, students will have a solid understanding of deep learning and its current applications in business.
The students will have frequent opportunities to work in groups on four mini-projects. These projects will involve applying the concepts and techniques learned in the lectures to real-world business examples. The goal of these projects is to give students hands-on experience with deep learning and help them build a portfolio of work to showcase their skills. By the end of the course, each student will have a collection of four mini-projects that demonstrate their ability to apply deep learning to solve business problems.
Literature
- Olah, Christopher. “Understanding lstm networks.” (2015).