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> A large part of ML today is very much an applied practical field. Agreed. Once you understand the difference between bias, variance, training, test and devel

> A large part of ML today is very much an applied practical field. Agreed. Once you understand the difference between bias, variance, training, test and development sets, cross-validation, feature selection, normalization, precision, recall, F-score, Matthews correlation coefficient, regularization, imputation techniques for missing values, overfitting, etc. I.e. you know how to build and test models in a rigorous fashion, you're 90% of the way there. Knowing what these terms mean, and why you need to understand them is waaaay more important than understanding the math behind SVM. It almost becomes boring at that point, because it's the same crap over and over. Doing actual AI research, that's something completely different. I mean just look at how very simple these Keras examples are, and these are really quite advanced and powerful deep learning models: https://github.com/keras-team/keras/tree/master/examples https://github.com/keras-team/keras/tree/master/examples. You definitely do _not_ need a PhD or even a Masters, to understand, re-implement or tweak on any of these models if you understand how to rigorously test the resulting model. Research: https://www.researchgate.net/publication/13853244_Long_Short-term_Memory https://www.researchgate.net/publication/13853244_Long_Short... Practice: model = Sequential() model.add(Embedding(max_features, 128, input_length=maxlen)) model.add(Bidirectional(LSTM(64))) model.add(Dropout(0.5)) model.add(Dense(1, activation='sigmoid'))

danielmorozoff 8y ago
I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience
- dagw 8y ago
  To answer that question and create a plan to tackle that problem requires knowledge and experience Sure, but you don't necessarily need knowledge about tensor fields and Fourier analysis.
- laichzeit0 8y ago
  What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You need a Computer Science degree to develop and debug software". The libraries, frameworks and operationalising of ML models is mature enough at this point that it's accessible to a wider audience than just people with research-level academic backgrounds. I'm not saying that your average web developer with no formal training can or even should be putting this kind of stuff in production. But someone with an undergraduate degree in Computer Science that's had a year or two of calculus and linear algebra and first year mathematical statistics should have no problem whatsoever in practice doing ML/AI. I mean look at this: from sklearn.model_selection import KFold # Define 10 fold cross-validation cv = KFold(n_splits=10) from sklearn.model_selection import GridSearchCV svm_model = GridSearchCV(svc_pipeline, param_grid=svc_parameters, scoring='f1_micro', cv=cv) svm_model.fit(X_train, y_train) A few lines and you're doing hyperparameter optimization on an SVM model with cross-validation. What a time to be alive.

danielmorozoff 8y ago

I think expertise is assessed not by just using, but building or fixing things that are broken. If a toy model/example are good enough for you then sure, use your LSTM / keras implementation. But if you're faced with your model not working in your usecase -- what do you do? To answer that question and create a plan to tackle that problem requires knowledge and experience

dagw 8y ago
To answer that question and create a plan to tackle that problem requires knowledge and experience Sure, but you don't necessarily need knowledge about tensor fields and Fourier analysis.
laichzeit0 8y ago
What I'm actually getting at is that AI/ML is now at the point where if one says "You need a Master's/PhD to do this sort of stuff" then it's like saying "You need a Computer Science degree to develop and debug software". The libraries, frameworks and operationalising of ML models is mature enough at this point that it's accessible to a wider audience than just people with research-level academic backgrounds. I'm not saying that your average web developer with no formal training can or even should be putting this kind of stuff in production. But someone with an undergraduate degree in Computer Science that's had a year or two of calculus and linear algebra and first year mathematical statistics should have no problem whatsoever in practice doing ML/AI. I mean look at this: from sklearn.model_selection import KFold # Define 10 fold cross-validation cv = KFold(n_splits=10) from sklearn.model_selection import GridSearchCV svm_model = GridSearchCV(svc_pipeline, param_grid=svc_parameters, scoring='f1_micro', cv=cv) svm_model.fit(X_train, y_train) A few lines and you're doing hyperparameter optimization on an SVM model with cross-validation. What a time to be alive.