Bẹrẹ nibi
Olubere · 4 Awọn modulu
Awọn ipilẹ AI
Loye ohun ti AI jẹ, bawo ni awọn ọna ṣiṣe ṣe kọ ẹkọ, ibiti wọn kuna, ati bii o ṣe le ṣe idajọ awọn ẹtọ laisi ariwo.
~4H lati pariWo dajudaju
Ile-ikawe AI ọfẹ
Kọ AI. Ofe lailai.
1657 Awọn itọsona Gẹẹsi pẹtẹlẹ, awọn ọna ikẹkọ ti iṣeto, ati ile-ikawe ṣiṣi - ti a ṣe nipasẹ ominira 501(c)(3) ti ko ni ere ki ẹnikẹni le loye AI ode oni.
- 1657Awọn itọsọna ọfẹ
- 9Awọn orin koko
- ~3 minFun itọnisọna
- ~74hAkoko kika
Bẹrẹ nibi
Marun Awọn ẹkọ ti o da lori abajade
Ẹkọ kọọkan pẹlu awọn abajade ti o han gbangba, awọn ọgbọn maapu, awọn iṣẹ adaṣe, ati capstone ti a lo.
Olubere · 4 Awọn modulu
Lodidi AI User
Lo AI ni iṣelọpọ lakoko ti o daabobo aṣiri, ṣayẹwo awọn abajade, ati fifipamọ ojuse eniyan.
~5H lati pariWo dajudajuAgbedemeji · 4 Awọn modulu
AI ni Iṣẹ
Ṣe ayẹwo awọn ọran lilo ibi iṣẹ, ṣiṣe awọn awakọ ailewu, wiwọn iye, ati ṣe ibaraẹnisọrọ awọn ayipada ni ojuse.
~6H lati pariWo dajudajuAgbedemeji · 4 Awọn modulu
AI Afihan & Awujọ
Ṣe itupalẹ awọn eto AI nipasẹ awọn ẹtọ, inifura, iṣakoso, aabo, ati awọn abajade iwulo ti gbogbo eniyan.
~6H lati pariWo dajudajuAgbedemeji · 4 Awọn modulu
Ilé pẹlu AI Systems
Loye awọn awoṣe ede, gbigba, awọn aṣoju, igbelewọn, idiyele, ati awọn aabo imuṣiṣẹ nipasẹ apẹrẹ eto to wulo.
~8H lati pariWo dajudaju
Awọn orin koko
Ṣawakiri nipasẹ orin
Lọ si agbegbe ti o bikita. Gbogbo orin ni ọpọ itọsona-Gẹẹsi.
- 91 awọn itọsọnaAwọn ipilẹAwọn imọran pataki: bawo ni AI ṣe n ṣiṣẹ ni ipele giga.Ṣàwárí
- 177 awọn itọsọnaÈdè AILLMs, NLP, ChatGPT, titan, ati oye ọrọ.Ṣàwárí
- 170 awọn itọsọnaAI wiwoIran aworan, iran kọmputa, fidio, ati apẹrẹ.Ṣàwárí
- 124 awọn itọsọnaAudio AIOhùn, orin, adarọ-ese, ati ohun-akọkọ AI.Ṣàwárí
- 123 awọn itọsọnaAwọn ile-iṣẹAwọn profaili ti OpenAI, Anthropic, Google, Meta, ati diẹ sii.Ṣàwárí
- 346 awọn itọsọnaAwọn ohun eloBii AI ṣe afihan ni ifaminsi, wiwa, awọn aṣoju, ati igbesi aye ojoojumọ.Ṣàwárí
- 165 awọn itọsọnaAwọn ile-iṣẹAI ni ilera, iṣuna, eto-ẹkọ, agbara, ati diẹ sii.Ṣàwárí
- 319 awọn itọsọnaImọ-ẹrọAwọn eerun igi, atunṣe-daradara, RAG, awọn olutọpa, ati awọn amayederun AI.Ṣàwárí
- 142 awọn itọsọnaAwujoIyatọ, asiri, aṣẹ lori ara, ailewu, ati ojo iwaju AI.Ṣàwárí
Ile-ikawe ni kikun
Gbogbo awọn itọsọna
1657 ti 1657 awọn itọsọna han. Àlẹmọ nipasẹ orin tabi wiwa loke.
- Imọ-ẹrọFarasin Technical Gbese ni ML SystemsAwọn eto ML le ṣajọpọ gbese imọ-ẹrọ ju idiju koodu lasan nitori awọn awoṣe da lori iyipada data, awọn ẹya, awọn opo gigun ati awọn yipo esi.3 min kaKa
- Imọ-ẹrọIsediwon Ẹya Ohun pẹlu LibrosaLibrosa is a Python library for loading audio and computing signal features such as spectrograms, MFCCs, chroma, and onset strength.3 min kaKa
- Imọ-ẹrọAbojuto ML pẹlu Prometheus ati GrafanaPrometheus can collect numeric time-series metrics exposed by model services, while Grafana visualizes those metrics in dashboards and alerts.3 min kaKa
- Imọ-ẹrọAabo Pq Ipese ML ati Ibuwọlu AwoṣeAabo ipese-pq ML ṣe aabo koodu, awọn igbẹkẹle, awọn ipilẹ data ati awọn ohun-ọṣọ awoṣe lati fifọwọkan tabi awọn orisun ti a ko gbẹkẹle jakejado kikọ ati imuṣiṣẹ.3 min kaKa
- Imọ-ẹrọAwọn ipele Ìbàlágà MLOpsMLOps maturity models describe how teams evolve from manual model development toward repeatable automation for testing, deployment and retraining.3 min kaKa
- Imọ-ẹrọAwọn ẹnubode Afọwọsi Awoṣe Ṣaaju IfiranṣẹModel validation gates are explicit checks a candidate must pass before it can move into a higher-risk deployment stage.3 min kaKa
- Imọ-ẹrọPitch Shifting and Time StretchingPitch shifting changes perceived fundamental frequency while aiming to preserve duration, whereas time stretching changes duration while aiming to preserve…3 min kaKa
- Imọ-ẹrọWiwọle Asọtẹlẹ ati Gbigba Data ItọkasiPrediction logging records enough context to audit, debug and later evaluate model decisions, including timestamps, model versions, inputs or privacy-safe…3 min kaKa
- Imọ-ẹrọPython Iṣakoso Igbẹkẹle fun MLPython dependency management records which interpreters and packages an ML project needs and how those versions are resolved for each environment.3 min kaKa
- Imọ-ẹrọTerraform fun ML InfrastructureTerraform describes cloud infrastructure as code and creates or updates resources by comparing configuration with recorded state and provider APIs.3 min kaKa
- Imọ-ẹrọTriton Language for Custom GPU KernelsTriton is a Python-based language and compiler for writing GPU kernels in terms of blocks of elements rather than individual hardware threads.3 min kaKa
- Imọ-ẹrọUnit Igbeyewo Machine Learning CodeAwọn idanwo ipin fun koodu ML ṣayẹwo kekere, awọn apakan ipinnu ti opo gigun ti epo gẹgẹbi iṣaju, awọn apẹrẹ tensor, awọn iṣiro pipadanu ati awọn atọkun asọtẹlẹ.3 min kaKa
Pari kika? Fi ẹ̀rí hàn pé.
Ṣayẹwo ohun ti o kọ pẹlu ibeere ibeere koko kan, lẹhinna ṣawari awọn iṣẹ ikẹkọ ti a ṣeto tabi ṣiṣẹ si ijẹrisi kan. Gbogbo guide duro free lati ka.