Code Implementation from Research Paper (Machine Learning)

Beginner level crouse

 

Paper implementation is the process of recreating or running a paper's given codebase shared via GitHub or any other code sharing platform, so that users can recreate the results shared in the papers and get a better understanding of what that paper is contributing to the research field. In this course, we will use Google Colab as our recreating platform. Implementation of different papers is an important part and the very first step of setting your goals for your thesis/project research. By the end of the course, you will learn how to recreate the environments in Google Colab, test the Deep Learning models, how to retrain them and learn how to debug different types of common errors along the way.

 

What will I learn?

In this course, you will learn how to implement code from research papers in the field of machine learning. You will understand the latest research in machine learning and learn how to create code that is based on this research. You will learn how to find research papers, read and understand them, and then implement the code and algorithms described in these papers. This course is designed for anyone interested in staying current with the latest research in machine learning.

Instructor

Md. Safirur Rashid

Machine Learning Engineer at
AlterSense Limited

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