Neural Network

Today, I will share all the questions I encountered during the interview and share how to answer them. Some of these questions are relatively normal and have a certain theoretical background, but some are very innovative.

Machine learning uses algorithms to extract information from raw data and present it in some type of model. We use this model to infer other data that has not been modeled.

The performance of different machine learning algorithms depends on the size and structure of the data. Therefore, unless we use traditional trial and error experiments, we have no clear way to prove that a choice is right.

In this article, I want to share with you 8 neural network architectures. I believe that any machine learning researcher should be familiar with this process to promote their work.

The most important algorithm is the neural network, which is not very successful due to overfitting (the model is too powerful, but the data is insufficient). Still, in some more specific tasks, the idea of using data to adapt to functionality has achieved significant success, and this also forms the basis of today's machine learning.