Improving Model Performance (C3W1L01)

Key Takeaways

hi welcome to this course on how to structure your machine learning project that is on machine learning strategy I hope that through this course you learn how to much more quickly and efficiently get your machine learning systems working so what is machine learning strategy let's start with a motivating example let's say you are working on your cat crossfire and after working for some time you've gotten your system to have 90% accuracy but this isn't good enough for your application you might don't have a lot of ideas for how to improve your system for example you might think well let's collect more data more training data or you might say maybe your training set isn't diverse enough yet you should collect images of cats and more diverse poses or maybe a more diverse set of negative examples well maybe you want to train the album longer with gradient descents or maybe you want to try a different optimization algorithm like the atom optimization algorithm or maybe try a bigger network or smaller network or maybe you want to try on drop out or maybe l2 regularization or maybe you want to change the network architecture such as trying activation functions change number of hidden units and so on and so on when trying to improve a deep learning system you often have a lot of ideas but things you could try and the problem is that if you choose poorly it is entirely possible the you end up spending six months charging in some direction only to realize after six months that that didn't do any good for example I've seen some teams spend literally six months collecting more data only to realize after six months that it barely improves the performance of their system so assuming you don't have six months to wait on your problem won't it be nice if you had quick and effective ways to figure out which of all of these ideas and maybe even other ideas are worth pursuing and which ones you can safely discard so what I hope to do in this course is teach you a number of strategies that is ways of analyzing a machine learning problem they'll help point you in the direction of the most promising things to try what I'll doing this courses also share of you a number of lessons I've learned through building and shipping you know large number of deep learning products and I think these materials are actually quite unique to this cause I don't see a lot of these ideas being taught in universities deep learning courses for example it turns out also that machine learning strategy is changing in the area of deep learning because the things you could do are now different with deep learning algorithms than with previous generation of machine learning algorithms but I hope that these ideas will help you become much more effective at getting your deep learning system to work