>>After much trial and error I eventually learned that this is often the case of a training set that has not been correctly randomized and is a problem when you are using stochastic gradient algorithms that process the data in small batches.
Take this single term from the above sentence: "stochastic gradient algorithms", they represent three key areas: statistics, calculus and CS.
These three things, even when they are to be studied in isolation are much complex. For ML, you must be able to juggle these 3 fireballs effectively. No surprise, it's much, much more difficult than many other software engineering problems.
>>After much trial and error I eventually learned that this is often the case of a training set that has not been correctly randomized and is a problem when you are using stochastic gradient algorithms that process the data in small batches.
Take this single term from the above sentence: "stochastic gradient algorithms", they represent three key areas: statistics, calculus and CS.
These three things, even when they are to be studied in isolation are much complex. For ML, you must be able to juggle these 3 fireballs effectively. No surprise, it's much, much more difficult than many other software engineering problems.