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ASIN ‏ : ‎ B09SXNZG8X
Publisher ‏ : ‎ Springer (February 18, 2022)
Publication date ‏ : ‎ February 18, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 60868 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 343 pages

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Deep generative modeling is a powerful technique in machine learning that involves using neural networks to generate new data samples that closely resemble the training data. This approach is particularly useful for tasks such as image generation, text generation, and music generation.

One of the key advantages of deep generative modeling is its ability to capture complex patterns and dependencies in the data, allowing for the creation of high-quality, realistic samples. This is achieved through the use of deep neural networks, which are capable of learning intricate structures in the data and generating new samples that exhibit similar characteristics.

Deep generative modeling has a wide range of applications, including image synthesis, data augmentation, and anomaly detection. By training a deep generative model on a large dataset, researchers can generate new samples that can be used for various purposes, such as creating training data for machine learning models or generating new content for creative projects.

Overall, deep generative modeling is a powerful and versatile technique that has the potential to revolutionize the way we generate and interact with data. By harnessing the power of deep neural networks, researchers can create realistic and high-quality samples that can be used for a variety of applications.
#Deep #Generative #Modeling,generative ai and llms: natural language processing and generative
adversarial networks