We may earn an affiliate commission when you visit our partners.
Google Cloud Training
Questo corso introduce i concetti di interpretabilità e la trasparenza dell'AI. Parla dell'importanza della trasparenza dell'AI per sviluppatori ed engineer. Illustra metodi e strumenti pratici per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei modelli di AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/9fy5rc/responsible
What's inside
Syllabus
Introduzione al corso
Questo modulo introduce la struttura e gli obiettivi del corso.
Interpretabilità e trasparenza dell'AI
Questo modulo è incentrato su interpretabilità e trasparenza nell'AI. Fornisce varie tecniche e strumenti per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei modelli di AI.
Read more
Syllabus
Introduzione al corso
Questo modulo introduce la struttura e gli obiettivi del corso.
Interpretabilità e trasparenza dell'AI
Questo modulo è incentrato su interpretabilità e trasparenza nell'AI. Fornisce varie tecniche e strumenti per aiutare a raggiungere interpretabilità e trasparenza sia nei dati che nei modelli di AI.
Read more
Riepilogo del corso
Questo modulo fornisce un riepilogo dell'intero corso coprendo i concetti, gli strumenti e le tecnologie più importanti.
Risorse del corso
Link PDF per gli studenti a tutti i moduli
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Il corso illustra i concetti di interpretabilità e trasparenza dell'AI, argomenti fondamentali per sviluppatori e tecnici del settore
Il corso offre metodi e strumenti per raggiungere trasparenza e interpretabilità in dati e modelli di AI
Il corso è erogato da Google Cloud Training, esperti riconosciuti nel campo dell'intelligenza artificiale
Save this course
Save Responsible AI for Developers: Interpretability & Transparency - Italiano to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Responsible AI for Developers: Interpretability & Transparency - Italiano with these
activities:
Review basic machine learning concepts
Show steps
Refresh your understanding of machine learning before the course begins to ensure that you are prepared for success.
Browse courses on
Machine Learning
Show steps
-
Read through your notes or study materials from previous courses in machine learning.
-
Do practice questions to test your understanding of the fundamental concepts.
-
Attend a workshop or online tutorial on machine learning.
Rivedi i concetti fondamentali di machine learning
Show steps
Una solida comprensione dei concetti fondamentali del machine learning ti aiuterà a comprendere meglio i concetti di interpretabilità e trasparenza dell'AI.
Browse courses on
Machine Learning
Show steps
-
Rivedi i materiali del tuo corso precedente di machine learning.
-
Completa gli esercizi di ripasso forniti.
-
Partecipa a un workshop di ripasso di machine learning.
Follow tutorials on interpretability and transparency in AI
Show steps
Supplement your learning with tutorials to gain a deeper understanding of interpretability and transparency in AI.
Browse courses on
Interpretability
Show steps
-
Search for online tutorials on interpretability and transparency in AI.
-
Follow the tutorials and complete the exercises provided.
-
Take notes and record your learnings.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice interpreting AI models
Show steps
Develop your skills in interpreting AI models to gain a better understanding of their predictions and limitations.
Browse courses on
Interpretability
Show steps
-
Find a dataset and create a simple AI model.
-
Use interpretability techniques to analyze the model's predictions.
-
Write a report summarizing your findings.
Develop a tool or resource to explain interpretability and transparency in AI
Show steps
Demonstrate your understanding by creating a tool or resource that can help others learn about interpretability and transparency in AI.
Browse courses on
Interpretability
Show steps
-
Brainstorm ideas for a tool or resource that could help explain interpretability and transparency in AI.
-
Develop a plan for creating your tool or resource.
-
Create your tool or resource.
-
Share your tool or resource with others.
Participate in a hackathon or competition on interpretability or transparency in AI
Show steps
Challenge yourself and test your skills by participating in a hackathon or competition focused on interpretability or transparency in AI.
Browse courses on
Interpretability
Show steps
-
Find a hackathon or competition that aligns with your interests.
-
Form a team or work independently.
-
Develop a solution to the challenge.
-
Present your solution and compete against other participants.
Contribute to an open-source project on interpretability or transparency in AI
Show steps
Contribute to the development of interpretability and transparency in AI by participating in open-source projects.
Browse courses on
Interpretability
Show steps
-
Find an open-source project that aligns with your interests.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute.
-
Submit a pull request with your contribution.
Review basic machine learning concepts
Show steps
Refresh your understanding of machine learning before the course begins to ensure that you are prepared for success.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read through your notes or study materials from previous courses in machine learning.
- Do practice questions to test your understanding of the fundamental concepts.
- Attend a workshop or online tutorial on machine learning.
Rivedi i concetti fondamentali di machine learning
Show steps
Una solida comprensione dei concetti fondamentali del machine learning ti aiuterà a comprendere meglio i concetti di interpretabilità e trasparenza dell'AI.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Rivedi i materiali del tuo corso precedente di machine learning.
- Completa gli esercizi di ripasso forniti.
- Partecipa a un workshop di ripasso di machine learning.
Follow tutorials on interpretability and transparency in AI
Show steps
Supplement your learning with tutorials to gain a deeper understanding of interpretability and transparency in AI.
Browse courses on
Interpretability
Show steps
Show steps
Show steps
- Search for online tutorials on interpretability and transparency in AI.
- Follow the tutorials and complete the exercises provided.
- Take notes and record your learnings.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice interpreting AI models
Show steps
Develop your skills in interpreting AI models to gain a better understanding of their predictions and limitations.
Browse courses on
Interpretability
Show steps
Show steps
Show steps
- Find a dataset and create a simple AI model.
- Use interpretability techniques to analyze the model's predictions.
- Write a report summarizing your findings.
Develop a tool or resource to explain interpretability and transparency in AI
Show steps
Demonstrate your understanding by creating a tool or resource that can help others learn about interpretability and transparency in AI.
Browse courses on
Interpretability
Show steps
Show steps
Show steps
- Brainstorm ideas for a tool or resource that could help explain interpretability and transparency in AI.
- Develop a plan for creating your tool or resource.
- Create your tool or resource.
- Share your tool or resource with others.
Participate in a hackathon or competition on interpretability or transparency in AI
Show steps
Challenge yourself and test your skills by participating in a hackathon or competition focused on interpretability or transparency in AI.
Browse courses on
Interpretability
Show steps
Show steps
Show steps
- Find a hackathon or competition that aligns with your interests.
- Form a team or work independently.
- Develop a solution to the challenge.
- Present your solution and compete against other participants.
Contribute to an open-source project on interpretability or transparency in AI
Show steps
Contribute to the development of interpretability and transparency in AI by participating in open-source projects.
Browse courses on
Interpretability
Show steps
Show steps
Show steps
- Find an open-source project that aligns with your interests.
- Review the project's documentation and codebase.
- Identify an area where you can contribute.
- Submit a pull request with your contribution.
Career center
Learners who complete Responsible AI for Developers: Interpretability & Transparency - Italiano will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/9fy5rc/responsible
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/9fy5rc/responsible
Share
Similar courses
Similar courses are unavailable at this time. Please try again later.
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024
- September 23, 2024
Language
Italiano
Transcript
English
Good to know
Il corso illustra i concetti di interpretabilità e trasparenza dell'AI, argomenti fondamentali per sviluppatori e tecnici del settore
Il corso offre metodi e strumenti per raggiungere trasparenza e interpretabilità in dati e modelli di AI
Il corso è erogato da Google Cloud Training, esperti riconosciuti nel campo dell'intelligenza artificiale
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2025 OpenCourser