This week mostly consisted of our learning and deep learning primer phase where we familiarise ourselves with the content that we’ll need to understand to implement the ideal ML algorithm we can use. Over the week I reviewed the 10-601 Introduction to Machine Learning recorded lectures and relevant homeworks such as neural networks and logistic regression. Antonis shared a 10-701 footprint recognition assignment with us because of the similarity to our project so I searched the assignment for inspiration on techniques we can incorporate. I also researched popular edge detection and feature extraction algorithms for the preprocessing stage of the workflow. \u00a0Next week, we’ll plan to start implementing these two techniques and write different types of feature detectors so we can compare their performance later on.<\/p>\n","protected":false},"excerpt":{"rendered":"
This week mostly consisted of our learning and deep learning primer phase where we familiarise ourselves with the content that we’ll need to understand to implement the ideal ML algorithm we can use. Over the week I reviewed the 10-601 Introduction to Machine Learning recorded lectures and relevant homeworks such as neural networks and logistic … <\/p>\n