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Deep Learning and Optimization

In: Synthetic Data for Deep Learning

Author

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  • Sergey I. Nikolenko

    (Synthesis AI
    Steklov Institute of Mathematics)

Abstract

Deep learning is currently one of the hottest fields not only in machine learning but in the whole of science. Since the mid-2000s, deep learning models have been revolutionizing artificial intelligence, significantly advancing state of the art across all fields of machine learning: computer vision, natural language processing, speech and sound processing, generative models, and much more. This book concentrates on synthetic data applications; we cannot hope to paint a comprehensive picture of the entire field and refer the reader to other books for a more detailed overview of deep learning [153, 289, 630, 631]. Nevertheless, in this chapter, we begin with an introduction to deep neural networks, describing the main ideas in the field. We especially concentrate on approaches to optimization in deep learning, starting from regular gradient descent and working our way towards adaptive gradient descent variations and state-of-the-art ideas.

Suggested Citation

  • Sergey I. Nikolenko, 2021. "Deep Learning and Optimization," Springer Optimization and Its Applications, in: Synthetic Data for Deep Learning, chapter 0, pages 19-58, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-75178-4_2
    DOI: 10.1007/978-3-030-75178-4_2
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    Cited by:

    1. Kentaro Hoffman & Stephen Salerno & Jeff Leek & Tyler McCormick, 2024. "Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models," Papers 2405.13926, arXiv.org.
    2. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.

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