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זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם.
Register for this course and see more details by visiting:
OpenCourser.com/course/wqd59e/introduction
What's inside
Syllabus
מבוא לבינה מלאכותית גנרטיבית
זהו קורס מבוא ממוקד שמטרתו להסביר מהי בינה מלאכותית גנרטיבית, איך משתמשים בה ובמה היא שונה משיטות מסורתיות של למידת מכונה. הוא גם כולל הסבר על הכלים של Google שיעזרו לכם לפתח אפליקציות בינה מלאכותית גנרטיבית משלכם
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces fundamental principles of generative artificial intelligence
Guides practical applications of generative artificial intelligence
Provides exclusive access to Google tools for developing applications using generative artificial intelligence
Taught by Google Cloud Training, a reputable organization in the field
No explicit prerequisites, allowing for flexibility in learning
Save this course
Save Introduction to Generative AI - בעברית to your list so you can find it easily later:
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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 Introduction to Generative AI - בעברית with these
activities:
Find a mentor who is experienced in GANs
Show steps
Having a mentor can provide personalized guidance and support in your GAN learning journey.
Show steps
-
Identify potential mentors through networking or online platforms.
-
Reach out to mentors and ask for their guidance.
Read 'Generative Adversarial Networks' by Ian Goodfellow
Show steps
This book provides a comprehensive overview of GANs and their applications.
View
Deep Learning. Das umfassende Handbuch
on Amazon
Show steps
Follow tutorials on GANs
Show steps
Following tutorials will provide additional guidance and support in understanding GANs.
Show steps
-
Find tutorials on GANs from reputable sources.
-
Follow the tutorials and complete the exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or online forum for GANs
Show steps
Joining a study group or online forum will provide opportunities for discussion and collaboration.
Show steps
-
Find a study group or online forum dedicated to GANs.
-
Participate in discussions and ask questions.
Practice GAN coding exercises
Show steps
Solving coding exercises will reinforce the concepts learned in the course and improve your programming skills.
Show steps
-
Find GAN coding exercises online or in textbooks.
-
Solve the exercises and test your solutions.
Create a GAN project
Show steps
Building a GAN project will provide hands-on experience with the concepts learned in the course.
Show steps
-
Choose a dataset to train your GAN on.
-
Implement the GAN architecture using a library like TensorFlow or PyTorch.
-
Train the GAN and evaluate its performance.
-
Deploy the GAN and use it to generate new data.
Create a presentation or report on GANs
Show steps
Creating a presentation or report will help you synthesize and communicate your understanding of GANs.
Show steps
-
Choose a topic related to GANs.
-
Research the topic and gather information.
-
Create a presentation or report that summarizes your findings.
Contribute to an open-source GAN project
Show steps
Contributing to an open-source project will provide practical experience and help you learn from others.
Show steps
-
Find an open-source GAN project that interests you.
-
Identify ways to contribute, such as fixing bugs or adding new features.
-
Submit your contributions to the project and get feedback.
Find a mentor who is experienced in GANs
Show steps
Having a mentor can provide personalized guidance and support in your GAN learning journey.
Show steps
Show steps
Show steps
- Identify potential mentors through networking or online platforms.
- Reach out to mentors and ask for their guidance.
Read 'Generative Adversarial Networks' by Ian Goodfellow
Show steps
This book provides a comprehensive overview of GANs and their applications.
View
Deep Learning. Das umfassende Handbuch
on Amazon
Show steps
Show steps
Show steps
Follow tutorials on GANs
Show steps
Following tutorials will provide additional guidance and support in understanding GANs.
Show steps
Show steps
Show steps
- Find tutorials on GANs from reputable sources.
- Follow the tutorials and complete the exercises.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or online forum for GANs
Show steps
Joining a study group or online forum will provide opportunities for discussion and collaboration.
Show steps
Show steps
Show steps
- Find a study group or online forum dedicated to GANs.
- Participate in discussions and ask questions.
Practice GAN coding exercises
Show steps
Solving coding exercises will reinforce the concepts learned in the course and improve your programming skills.
Show steps
Show steps
Show steps
- Find GAN coding exercises online or in textbooks.
- Solve the exercises and test your solutions.
Create a GAN project
Show steps
Building a GAN project will provide hands-on experience with the concepts learned in the course.
Show steps
Show steps
Show steps
- Choose a dataset to train your GAN on.
- Implement the GAN architecture using a library like TensorFlow or PyTorch.
- Train the GAN and evaluate its performance.
- Deploy the GAN and use it to generate new data.
Create a presentation or report on GANs
Show steps
Creating a presentation or report will help you synthesize and communicate your understanding of GANs.
Show steps
Show steps
Show steps
- Choose a topic related to GANs.
- Research the topic and gather information.
- Create a presentation or report that summarizes your findings.
Contribute to an open-source GAN project
Show steps
Contributing to an open-source project will provide practical experience and help you learn from others.
Show steps
Show steps
Show steps
- Find an open-source GAN project that interests you.
- Identify ways to contribute, such as fixing bugs or adding new features.
- Submit your contributions to the project and get feedback.
Career center
Learners who complete Introduction to Generative AI - בעברית will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers turn the inventions of scientists into products. This can range from making it easier for your phone to recognize your face, to building self-driving cars, to personalizing your shopping experience. By learning the basics of Generative AI, you will be better prepared to solve many of these challenges.
Data Analyst
Data Analyst
Data Analysts find and conclude trends using data. This role is vital for organizations that collect large amounts of data and want to use it to make better decisions.
Data Scientist
Data Scientist
Data Scientists use all of the latest algorithms to solve some of the world's most difficult problems, ranging from finding the optimal route for a delivery truck to helping doctors find the best way to diagnose a patient. This course helps teach you some of the foundational algorithms that data scientists will regularly use.
Risk Manager
Risk Manager
Risk Managers identify, assess, and mitigate risks. This course will teach you about some of the mathematical tools used to do this.
Data Engineer
Data Engineer
Data Engineers make the data that is used by data analysts accessible and useful. This course can help you to better understand what data is available to you and how to use it.
Actuary
Actuary
Actuaries use statistical methods to assess risk. This course could help prepare you for the types of statistical problems that you will solve in this role.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. The course can help you learn many new ways to best interpret data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts apply statistical methods to solve real-world problems. This often involves creating predictive models and using those models to aid in making important financial decisions. This course teaches you some of the methods used by analysts in order to build those predictive models.
Financial Analyst
Financial Analyst
Financial Analysts gather and interpret data to help guide financial decisions. This course could teach you about many of the models that you will use as a financial analyst.
Business Analyst
Business Analyst
Business Analysts use data to help companies solve their many problems. This ranges from choosing which new products to bring to market, to choosing how to market a product, to deciding how to better manage internal data.
Market Researcher
Market Researcher
Market Researchers seek to understand consumer behavior, which includes discovering the needs, wants, and motivations of target markets. By taking this course, you can learn about new methods to achieve this goal.
Software Engineer
Software Engineer
Software Engineers are the backbone of the digital revolution. This course would be helpful to anyone who wants to be involved with the bleeding edge of Generative AI as it develops.
Product Manager
Product Manager
Product Managers guide teams as they bring new products to market. The course teaches you about the many different skills that a Product Manager needs to be successful, including modeling and communicating the value of new features to stakeholders.
Investment Banker
Investment Banker
Investment Bankers evaluate and advise companies on potential investments. This course teaches you about the fundamentals of building the models used to do this.
Consultant
Consultant
Consulting includes many different career paths that consist of providing expert advice to companies, governments, and other organizations. This course can help you to gain the foundational skills you need to be successful as a consultant.
For more career information including salaries, visit:
OpenCourser.com/course/wqd59e/introduction
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Generative AI - בעברית.
Comprehensive treatment of deep learning for generative models, written by one of the field's leading researchers. It would make an excellent reference for anyone looking to build cutting-edge generative AI models.
Generative AI subset of deep reinforcement learning, and this book would be a helpful reference for those wishing to learn more about the broader field.
More in-depth look at causal inference, and like the previous book, is written by Judea Pearl, who is one of the world's leading experts on the subject. This book is extremely valuable to those specializing in generative AI.
Very practical computer science and programming-focused guide to building your own generative AI models in Python. It would be a good reference.
Classic textbook on statistical learning, including a good chapter on generative models. It would be a valuable reference for anyone wishing to pursue the subject more deeply.
This classic textbook on reinforcement learning, which subfield of generative AI. It would be a valuable resource for anyone wishing to learn more about this topic.
Provides a comprehensive mathematical background for machine learning, including a section on generative models. This book would provide essential background for advanced study in generative AI models.
Save
Discusses important issues to keep in mind when thinking about causation, which plays a key role in generative AI. This book would be useful for someone who is deep in the study of generative AI.
Save
Explores the potential impact of AI on the future of humanity. It valuable resource for anyone who wants to think about the ethical and social implications of AI.
Save
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about deep learning and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wqd59e/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
עברית
Transcript
English
Good to know
Introduces fundamental principles of generative artificial intelligence
Guides practical applications of generative artificial intelligence
Provides exclusive access to Google tools for developing applications using generative artificial intelligence
Taught by Google Cloud Training, a reputable organization in the field
No explicit prerequisites, allowing for flexibility in learning
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