#books #kagglebook #ctf
Читаю The Kaggle book, оказывается , Энтони Голдблюм по образованию экономист, как и я )
А Джереми Ховард перед основанием fast.ai трудился в Kaggle.
Хинтон тоже не избежал участия в соревах каггл, и даже выиграл MerckActiviy. Уже интересно!
"Professor Donoho does not refer to Kaggle specifically, but to all data science competition platforms. Quoting computational linguist Mark Liberman, he refers to data science competitions and platforms as being part of a Common Task Framework (CTF) paradigm that has been silently and steadily progressing data science in many fields during the last decades. He states that a CTF can work incredibly well at improving the solution of a problem in data science from an empirical point of view, quoting the Netflix competition and many DARPA competitions as successful examples. The CTF paradigm has contributed to reshaping the best-in-class solutions for problems in many fields.
The system works the best if the task is well defined and the data is of good quality. In the long run, the performance of solutions improves by small gains until it reaches an asymptote. The process can be sped up by allowing a certain amount of sharing among participants (as happens on Kaggle by means of discussions, and sharing Kaggle Notebooks and extra data provided by the datasets found in the Datasets section). According to the CTF paradigm, competitive pressure in a competition suffices to produce always-improving solutions. When the competitive pressure is paired with some degree of sharing among participants, the improvement happens at an even faster rate – hence why Kaggle introduced many incentives for sharing."
Читаю The Kaggle book, оказывается , Энтони Голдблюм по образованию экономист, как и я )
А Джереми Ховард перед основанием fast.ai трудился в Kaggle.
Хинтон тоже не избежал участия в соревах каггл, и даже выиграл MerckActiviy. Уже интересно!
"Professor Donoho does not refer to Kaggle specifically, but to all data science competition platforms. Quoting computational linguist Mark Liberman, he refers to data science competitions and platforms as being part of a Common Task Framework (CTF) paradigm that has been silently and steadily progressing data science in many fields during the last decades. He states that a CTF can work incredibly well at improving the solution of a problem in data science from an empirical point of view, quoting the Netflix competition and many DARPA competitions as successful examples. The CTF paradigm has contributed to reshaping the best-in-class solutions for problems in many fields.
The system works the best if the task is well defined and the data is of good quality. In the long run, the performance of solutions improves by small gains until it reaches an asymptote. The process can be sped up by allowing a certain amount of sharing among participants (as happens on Kaggle by means of discussions, and sharing Kaggle Notebooks and extra data provided by the datasets found in the Datasets section). According to the CTF paradigm, competitive pressure in a competition suffices to produce always-improving solutions. When the competitive pressure is paired with some degree of sharing among participants, the improvement happens at an even faster rate – hence why Kaggle introduced many incentives for sharing."