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Generative AI – An Overview. Software, #1
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- FormatePub
- ISBN8231303373
- EAN9798231303373
- Date de parution17/06/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurWalzone Press
Résumé
Generative Artificial Intelligence, an Artificial Intelligence concept which is used to create own data which includes creating text, audio, video, images, programming codes and also advanced data structures such as protein structures. Before the Generative AI, Artificial Intelligence models used Machine learning models to predict pattern and structures. Generative AI models are widely being used in Business, healthcare, education and media sectors.
Generative AI helps organizations to be more innovative, cost effective and efficient in their operations. Machine learning, Deep Learning, Large Language Models, transformers forms the basis for creating own data in Generative AI environment. Generative AI or Gen AI uses large data set to frame set of rules which is used in creating of new content. ChatGPT and Microsoft Copilot are one of the examples of the Generative AI tools which helps user to create texts and develop answers to users questions.
Both proprietary as well as open source Generative aI models are available now. Large Language Models or foundation models which are training large datasets forms the basis of creating contents in Generative AI. These models do not need to be trained and their trained on their own and this is the major difference between supervised Machine learning models which require labeling for classifying the data.
Generative AI model building starts with the data collection and processing of large datasets, then moving to model building, training and pattern learning from those large datasets and then finally rendering and validating different created the different types of contents such as text, audio, video and images. The book also provides overview of Python programming for the readers who are first time learners of Python programming.
The contents are adopted from Author's book on Python programming for Data Scientists
Generative AI helps organizations to be more innovative, cost effective and efficient in their operations. Machine learning, Deep Learning, Large Language Models, transformers forms the basis for creating own data in Generative AI environment. Generative AI or Gen AI uses large data set to frame set of rules which is used in creating of new content. ChatGPT and Microsoft Copilot are one of the examples of the Generative AI tools which helps user to create texts and develop answers to users questions.
Both proprietary as well as open source Generative aI models are available now. Large Language Models or foundation models which are training large datasets forms the basis of creating contents in Generative AI. These models do not need to be trained and their trained on their own and this is the major difference between supervised Machine learning models which require labeling for classifying the data.
Generative AI model building starts with the data collection and processing of large datasets, then moving to model building, training and pattern learning from those large datasets and then finally rendering and validating different created the different types of contents such as text, audio, video and images. The book also provides overview of Python programming for the readers who are first time learners of Python programming.
The contents are adopted from Author's book on Python programming for Data Scientists
Generative Artificial Intelligence, an Artificial Intelligence concept which is used to create own data which includes creating text, audio, video, images, programming codes and also advanced data structures such as protein structures. Before the Generative AI, Artificial Intelligence models used Machine learning models to predict pattern and structures. Generative AI models are widely being used in Business, healthcare, education and media sectors.
Generative AI helps organizations to be more innovative, cost effective and efficient in their operations. Machine learning, Deep Learning, Large Language Models, transformers forms the basis for creating own data in Generative AI environment. Generative AI or Gen AI uses large data set to frame set of rules which is used in creating of new content. ChatGPT and Microsoft Copilot are one of the examples of the Generative AI tools which helps user to create texts and develop answers to users questions.
Both proprietary as well as open source Generative aI models are available now. Large Language Models or foundation models which are training large datasets forms the basis of creating contents in Generative AI. These models do not need to be trained and their trained on their own and this is the major difference between supervised Machine learning models which require labeling for classifying the data.
Generative AI model building starts with the data collection and processing of large datasets, then moving to model building, training and pattern learning from those large datasets and then finally rendering and validating different created the different types of contents such as text, audio, video and images. The book also provides overview of Python programming for the readers who are first time learners of Python programming.
The contents are adopted from Author's book on Python programming for Data Scientists
Generative AI helps organizations to be more innovative, cost effective and efficient in their operations. Machine learning, Deep Learning, Large Language Models, transformers forms the basis for creating own data in Generative AI environment. Generative AI or Gen AI uses large data set to frame set of rules which is used in creating of new content. ChatGPT and Microsoft Copilot are one of the examples of the Generative AI tools which helps user to create texts and develop answers to users questions.
Both proprietary as well as open source Generative aI models are available now. Large Language Models or foundation models which are training large datasets forms the basis of creating contents in Generative AI. These models do not need to be trained and their trained on their own and this is the major difference between supervised Machine learning models which require labeling for classifying the data.
Generative AI model building starts with the data collection and processing of large datasets, then moving to model building, training and pattern learning from those large datasets and then finally rendering and validating different created the different types of contents such as text, audio, video and images. The book also provides overview of Python programming for the readers who are first time learners of Python programming.
The contents are adopted from Author's book on Python programming for Data Scientists
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