Structuring ML Projects

This is the second week of the third course of DeepLearning.AI’s Deep Learning Specialization offered on Coursera. This course is less technical than the previous two and focuses instead on general principles and intuition related to machine learning projects. This week’s topics are: Error Analysis Carrying out Error Analysis Cleaning up Incorrectly Labeled Data Build our First System Quickly, then Iterate Mismatched Training and Dev/Test Sets Training and Testing on Different Distributions Bias and Variance with Mismatched Data Distributions Addressing Data Mismatch Learning from Multiple Tasks Transfer Learning Multitask Learning End-to-end Deep Learning What is End-to-end Deep Learning? Whether to use End-to-end Deep Learning Error Analysis Carrying out Error Analysis One of the things that we can do when our model is performing worse than human-level performance is to carry out error analysis. Error analysis is just a fancy name to trying to ascertain what the sources of errors are. This is critical because if we can quickly come up with a “ceiling” or upper-bound on the improvement of a particular strategy. ...

This is the first week of the third course of DeepLearning.AI’s Deep Learning Specialization offered on Coursera. This course is less technical than the previous two and focuses instead on general principles and intuition related to machine learning projects. This week’s topics are: Introduction to ML Strategy Why ML Strategy Orthogonalization Setting Up our Goal Single Number Evaluation Metric Satisficing and Optimizing Metrics Train/Dev/Test Distributions Size of Dev and Test Sets When to Change Dev/Test Sets and Metrics? Comparing to Human-Level Performance Why Human-level performance? Avoidable Bias Understanding Human-level Performance Surpassing Human-level Performance Improving your Model Performance Introduction to ML Strategy Why ML Strategy Whenever we are working on a machine learning project, and after we have completed our first iteration on the approach, there might be many things to try next. Should we get more data? Try regularization? Try a bigger network? So many things to choose from. This is why we need high-level heuristics to guide our strategy. ...