' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='.143' stop-color='%23d5effc'/%3E%3Cstop offset='.842' stop-color='%233c8ec6'/%3E%3Cstop offset='1' stop-color='%2339c'/%3E%3C/radialGradient%3E%3Cpath fill='url(%23a)' d='M67.823 57.787c.154-1.553 1.451-20.462-14.797-29.05-7.596-4.018-17.848-3.964-23.477 1.74-9.948 10.068-1.514 20.621 1.642 21.669.12.035.075-.047.044-.174-4.996-19.942 29.742-23.512 35.881 5.71.218 1.035.615 1.011.707.105z'/%3E%3CradialGradient id='b' cx='475.332' cy='-726.447' r='19.084' gradientTransform='matrix(1 0 0 -1 -432 -666)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='.143' stop-color='%23fee588'/%3E%3Cstop offset='.564' stop-color='%23f79b30'/%3E%3Cstop offset='.687' stop-color='%23f58b2d'/%3E%3Cstop offset='.888' stop-color='%23f3752a'/%3E%3Cstop offset='1' stop-color='%23f36c29'/%3E%3C/radialGradient%3E%3Cpath fill='url(%23b)' d='M27.743 81.9c1.55.147 20.459 1.439 29.049-14.805 4.018-7.599 3.963-17.853-1.737-23.479-10.073-9.947-20.625-1.516-21.673 1.646-.025.065.074.065.172.044 19.944-4.996 23.512 29.744-5.705 35.883-1.038.215-1.015.611-.106.711z'/%3E%3CradialGradient id='c' cx='-415.647' cy='-620.671' r='26.288' gradientTransform='matrix(0 -1 -1 0 -591.623 -371.307)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23fcb116'/%3E%3Cstop offset='.193' stop-color='%23fbb318'/%3E%3Cstop offset='.335' stop-color='%23f9b91c'/%3E%3Cstop offset='.461' stop-color='%23f6c323'/%3E%3Cstop offset='.577' stop-color='%23f4d12d'/%3E%3Cstop offset='.673' stop-color='%23f0e03a'/%3E%3Cstop offset='.744' stop-color='%23e5d534'/%3E%3Cstop offset='.865' stop-color='%23cfbd24'/%3E%3Cstop offset='1' stop-color='%23b19e11'/%3E%3C/radialGradient%3E%3Cpath fill='url(%23c)' d='M3.634 41.813c-.155 1.552-1.448 20.46 14.801 29.052 7.595 4.018 17.848 3.962 23.476-1.741 9.948-10.069 1.518-20.621-1.645-21.669-.131-.047-.088 0-.045.172C45.22 67.57 10.48 71.137 4.34 41.919c-.219-1.038-.613-1.011-.706-.106z'/%3E%3CradialGradient id='d' cx='473.191' cy='-709.119' r='26.288' gradientTransform='matrix(1 0 0 -1 -432 -666)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23a1c43a'/%3E%3Cstop offset='.455' stop-color='%23a0cc3a'/%3E%3Cstop offset='1' stop-color='%233c943a'/%3E%3C/radialGradient%3E%3Cpath fill='url(%23d)' d='M43.715 17.704c-1.549-.153-20.46-1.444-29.05 14.803-4.016 7.595-3.963 17.847 1.74 23.474 10.069 9.949 20.621 1.521 21.67-1.645.022-.09.022-.09-.172-.041-19.941 4.996-23.508-29.746 5.708-35.884 1.042-.216 1.01-.612.104-.707z'/%3E%3Cg opacity='.6'%3E%3CradialGradient id='e' cx='434.848' cy='-357.278' r='30.374' gradientTransform='matrix(1.0987 0 0 -1.6216 -441.9 -504.471)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23f9e788'/%3E%3Cstop offset='1' stop-color='%23fff'/%3E%3C/radialGradient%3E%3Cpath opacity='.38' fill='url(%23e)' d='M35.729 30.865c-2.779 0-5.424.53-7.83 1.492-7.272 9.464.331 18.803 3.291 19.79.12.035.075-.047.044-.174-3.052-12.189 8.743-18.262 19.776-14.465-3.684-4.064-9.163-6.643-15.281-6.643z'/%3E%3CradialGradient id='f' cx='434.85' cy='-357.281' r='30.373' gradientTransform='matrix(1.0987 0 0 -1.6216 -441.9 -504.471)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23f9e788'/%3E%3Cstop offset='1' stop-color='%23fff'/%3E%3C/radialGradient%3E%3Cpath opacity='.38' fill='url(%23f)' d='M33.381 45.264c-.025.065.074.065.172.044 12.048-3.02 18.119 8.458 14.6 19.377 4.67-3.473 7.668-8.848 7.668-14.88 0-2.354-.456-4.604-1.286-6.681-9.912-9.166-20.123-.97-21.154 2.14z'/%3E%3CradialGradient id='g' cx='434.851' cy='-357.277' r='30.368' gradientTransform='matrix(1.0987 0 0 -1.6216 -441.9 -504.471)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23f9e788'/%3E%3Cstop offset='1' stop-color='%23fff'/%3E%3C/radialGradient%3E%3Cpath opacity='.38' fill='url(%23g)' d='M40.266 47.455c-.131-.047-.088 0-.045.172 3.055 12.193-8.742 18.262-19.775 14.465 3.684 4.063 9.164 6.643 15.284 6.643a21.15 21.15 0 0 0 7.828-1.484c7.275-9.468-.327-18.813-3.292-19.796z'/%3E%3CradialGradient id='h' cx='434.848' cy='-357.278' r='30.373' gradientTransform='matrix(1.0987 0 0 -1.6216 -441.9 -504.471)' gradientUnits='userSpaceOnUse'%3E%3Cstop offset='0' stop-color='%23f9e788'/%3E%3Cstop offset='1' stop-color='%23fff'/%3E%3C/radialGradient%3E%3Cpath opacity='.38' fill='url(%23h)' d='M38.075 54.336c.022-.09.022-.09-.172-.041-12.045 3.016-18.115-8.46-14.597-19.376-4.672 3.468-7.67 8.846-7.67 14.886 0 2.345.458 4.595 1.285 6.669 9.912 9.167 20.124.971 21.154-2.138z'/%3E%3C/g%3E%3C/svg%3E)
ChatGPT Koneoppimisen insinööri kehotegeneraattori
Auttaa sinua parantamaan keskustelujesi älykkyyttä ja luonnollisuutta
Haluan sinun toimivan koneoppimisinsinöörinä. Kirjoitan joitain koneoppimiskäsitteitä, ja sinun tehtäväsi on selittää ne helposti ymmärrettävin termein. Tämä voi sisältää vaiheittaisten ohjeiden antamista mallin rakentamiseen, erilaisten tekniikoiden esittelyä visuaalisesti tai online-resurssien ehdottamista lisätutkimuksia varten. Ensimmäinen ehdotuspyyntöni on "Minulla on tietojoukko ilman tunnisteita. Mitä koneoppimisalgoritmia minun pitäisi käyttää?"
I want you to act as a machine learning engineer. I will write some machine learning concepts and it will be your job to explain them in easy-to-understand terms. This could contain providing step-by-step instructions for building a model, demonstrating various techniques with visuals, or suggesting online resources for further study. My first suggestion request is "I have a dataset without labels. Which machine learning algorithm should I use?"
