Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models

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ASIN ‏ : ‎ B0CTHPCMQ6
Publisher ‏ : ‎ Academic Press; 3rd edition (December 6, 2024)
Publication date ‏ : ‎ December 6, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 90604 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 1194 pages
Page numbers source ISBN ‏ : ‎ 0443292388

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Machine Learning: From the Classics to Deep Networks, Transformers, and Diffusion Models

Machine learning has come a long way since its inception, evolving from classic algorithms like linear regression and decision trees to more advanced models such as deep neural networks, transformers, and diffusion models.

The classic machine learning algorithms, such as linear regression and decision trees, laid the foundation for modern machine learning techniques. These algorithms are still widely used today for tasks such as regression, classification, and clustering. However, as data and computational power have grown, more complex models have emerged.

Deep neural networks, inspired by the structure of the human brain, have revolutionized machine learning. These networks are capable of learning intricate patterns in data and have been successfully applied to tasks such as image and speech recognition, natural language processing, and autonomous vehicles.

Transformers, introduced in 2017, have further pushed the boundaries of machine learning. These models are based on a self-attention mechanism, allowing them to process sequences of data more efficiently than traditional recurrent neural networks. Transformers have been instrumental in advancements in natural language processing, with models like BERT and GPT-3 achieving state-of-the-art performance on various tasks.

Diffusion models, a recent development in machine learning, leverage the principles of physics to model complex systems. These models have shown promise in generating high-quality images, videos, and text, as well as in solving challenging optimization problems.

As machine learning continues to advance, it is essential to stay updated on the latest developments and techniques. From the classics to deep networks, transformers, and diffusion models, there is a vast array of tools and algorithms available to tackle a wide range of tasks. By understanding and leveraging these models, we can unlock the full potential of machine learning in various applications.
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