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The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.
The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.
But perhaps using a cost function or a loss function is enough to call it machine learning. A machine just used an algorithm to learn another algorithm after all.
The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.
Regarding recommender systems I see many companies trying neural nets and so many other fancy ML stuff for things that - in AB-tests are always outperformed by basic rules.
I understand the fun it is to build stuff and to use the new hot stuff. And at least for many analysts and marketing people as well as shop product owners this is new hot shit.
I also understand, that it is way more easy to get management to hand out the big bucks for something that is the new rage, as they tend to read the respective soundbites in their manager magazines.
But as said - I see it underperforming in tests nearly all the time. Not only, but esp. if you take the costs of development and maintenance into account. These systems cost more to build, more to enhance, more to run and bring in less real business value 70 - 90 percent of all the times I have seen them.
But they are presented to management in shiny presentations from agencies that need to sell the new hot shit to their clients to show that they are relevant. Because, as said, management thinks they need it and are often not open to agencies telling them, that the business value could be better served otherwise. Because in the end for a manager it is often times more valuable to show a fancy state of the art project to his/her higher ups than creating real business value.
You probably don't need a neural network if your model has 5 inputs.
The approach they show in the end - it's still machine learning though? Exploring a space and finding parameters to optimize for a loss function (speed around the track), just not deep learning with neural nets.
The big difference between this and the other approach mentioned in the article is the model is a simple one that's easy to understand instead of a many layered neural network which is rather opaque.
I think the article may be better titled "You might not need neural networks."