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Introduction to Image Generation - Français
Google Cloud Training
Google Cloud Training
Ce cours présente les modèles de diffusion, une famille de modèles de machine learning qui s'est récemment révélée prometteuse dans le domaine de la génération d'images. Les modèles de diffusion trouvent leur origine dans la physique, et plus précisément dans la thermodynamique. Au cours des dernières années, ils ont gagné en popularité dans la recherche et l'industrie. Ils sont à la base de nombreux modèles et outils Google Cloud avancés de génération d'images. Ce cours vous présente les bases théoriques des modèles de diffusion, et vous explique comment les entraîner et les déployer sur Vertex AI.
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What's inside
Syllabus
Introduction à la génération d'image
Ce cours présente les modèles de diffusion, une famille de modèles de machine learning qui s'est récemment révélée prometteuse dans le domaine de la génération d'images. Ils sont à la base de nombreux modèles et outils Google Cloud avancés de génération d'images. Ce cours vous présente les bases théoriques des modèles de diffusion, et vous explique comment les entraîner et les déployer sur Vertex AI.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops skills for the generation of images, which are increasingly important for automation in industry
Provides a practical understanding of training and deploying diffusion models on Vertex AI
Its connection to Google Cloud Training ensures access to the latest developments and applications of image generation
The course also aligns with the growing demand for professionals with expertise in artificial intelligence and machine learning
Suitable for learners with both theoretical and practical backgrounds in machine learning and image processing
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Introduction to Image Generation - Français.
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Career center
Learners who complete Introduction to Image Generation - Français will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
These engineers build and maintain machine learning models and their supporting infrastructure. This course would be a great introduction to these models for a Machine Learning Engineer.
Graphic designer
Graphic designer
Graphic designers create visual concepts, using computer software or by hand, to communicate ideas that inspire, inform, and captivate consumers.
Photographer
Photographer
Photographers capture images of people, places, and things for a range of purposes, including artistic, commercial, scientific, and personal.
Product Manager
Product Manager
Product managers oversee the development and launch of new products or features. They work closely with engineers, designers, and marketers to ensure that the product meets the needs of the market.
Business Intelligence Analyst
Business Intelligence Analyst
Business intelligence analysts help organizations make better decisions by analyzing data and identifying trends.
Robotics Engineer
Robotics Engineer
Robotics engineers design, construct, operate, and maintain robots. An understanding of image generation techniques would be very useful for professionals in this field as they build robots that can interact with a visual world.
Data Scientist
Data Scientist
Data scientists use scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
Data Analyst
Data Analyst
Data analysts collect, process, and analyze data to extract meaningful insights and information.
Web Developer
Web Developer
Web developers create and maintain websites and web applications.
Market Researcher
Market Researcher
Market researchers gather and analyze data on consumer trends and preferences to identify marketing opportunities for a product or service.
Front-End Developer
Front-End Developer
Front-end developers are responsible for the design and implementation of the user interface of a website or application.
User Experience Designer
User Experience Designer
User experience designers create the overall experience for users when they interact with a product or service.
Software Developer
Software Developer
This role involves designing, developing, programming, implementing, testing, documenting, deploying, maintaining, and modifying computer software and its associated components like source code.
Computer and Information Research Scientist
Computer and Information Research Scientist
Professionals in this occupation conduct research about computer science. The research may be to develop new computer software or hardware or to increase the speed, efficiency, or capability of existing systems. This course may be useful as it introduces ML models used for image generation.
Computer Hardware Engineer
Computer Hardware Engineer
Individuals in this role design, develop, and test computer hardware, such as processors, circuit boards, and other electronic components. This course may be useful as it introduces models that can be used in hardware to develop new visual capabilities.
For more career information including salaries, visit:
OpenCourser.com/course/8k202f/introduction
Reading list
We've selected 14 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 Image Generation - Français.
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Provides a comprehensive overview of generative adversarial networks (GANs), a powerful class of generative models that have been widely used for image generation. It covers the theoretical foundations of GANs, training algorithms, and applications in various domains.
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Provides a comprehensive overview of deep learning techniques for computer vision tasks, including image classification, object detection, and semantic segmentation. It covers the theoretical foundations of deep learning, popular architectures, and applications in various domains.
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Provides a comprehensive overview of computer vision algorithms and applications, including image processing, feature extraction, and object recognition. It covers the theoretical foundations of computer vision, popular algorithms, and applications in various domains.
Provides a comprehensive overview of digital image processing techniques, including image enhancement, filtering, and segmentation. It covers the theoretical foundations of digital image processing, popular algorithms, and applications in various domains.
Provides a comprehensive overview of artificial intelligence techniques for computer vision tasks, including knowledge representation, reasoning, and planning. It covers the theoretical foundations of artificial intelligence, popular algorithms, and applications in various domains.
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Provides a comprehensive overview of computer graphics techniques, including 3D modeling, rendering, and animation. It covers the theoretical foundations of computer graphics, popular algorithms, and applications in various domains.
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Provides a comprehensive overview of deep learning techniques, including neural networks, convolutional neural networks, and recurrent neural networks. It covers the theoretical foundations of deep learning, popular architectures, and applications in various domains.
Provides a comprehensive overview of natural language processing techniques, including text processing, machine translation, and speech recognition. It covers the theoretical foundations of natural language processing, popular algorithms, and applications in various domains.
Provides a comprehensive overview of reinforcement learning techniques, including Markov decision processes, Q-learning, and policy gradients. It covers the theoretical foundations of reinforcement learning, popular algorithms, and applications in various domains.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers the theoretical foundations of information theory, popular algorithms, and applications in various domains.
Provides a comprehensive overview of probability and statistics. It covers the theoretical foundations of probability and statistics, popular algorithms, and applications in various domains.
Provides a comprehensive overview of algorithms. It covers the theoretical foundations of algorithms, popular algorithms, and applications in various domains.
Provides a comprehensive overview of data structures and algorithms. It covers the theoretical foundations of data structures and algorithms, popular data structures, and algorithms, and applications in various domains.
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Provides a comprehensive overview of operating systems. It covers the theoretical foundations of operating systems, popular operating systems, and applications in various domains.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8k202f/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcripts
Español
Français
Good to know
Develops skills for the generation of images, which are increasingly important for automation in industry
Provides a practical understanding of training and deploying diffusion models on Vertex AI
Its connection to Google Cloud Training ensures access to the latest developments and applications of image generation
The course also aligns with the growing demand for professionals with expertise in artificial intelligence and machine learning
Suitable for learners with both theoretical and practical backgrounds in machine learning and image processing
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