Math and Architectures of Deep Learning

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Math and Architectures of Deep Learning

Math and Architectures of Deep Learning

Krishnendu Chaudhury

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Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.

Inside Math and Architectures of Deep Learning you will find:
    Math, theory, and programming principles side by side
    Linear algebra, vector calculus and multivariate statistics for deep learning
    The structure of neural networks
    Implementing deep learning architectures with Python and PyTorch
    Troubleshooting underperforming models
    Working code samples in downloadable Jupyter notebooks

年:

2024

出版:

出版社:

Manning Publications

语言:

english

页:

552

文件:

PDF, 83.50 MB

IPFS: