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Look inside
The mathematical paradigms that underlie deep learning typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You’ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
about the technology
It’s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You’ll be free from blind reliance on prepackaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you’ll be glad you can quickly identify and fix problems.about the book
Math and Architectures of Deep Learning sets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.what's inside
- 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
This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques.
An amazing introduction to the gory details of Machine Learning.
The travel guide that reveals the mathematics behind the curtain of NumPy, PyTorch and the Python ML libraries.
Gives a unique perspective about machine learning and mathematical approaches.
An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.
The one book you need to get all the required mathematical skills for deep learning.
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Math and Architectures of Deep Learning ebook for free
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Math and Architectures of Deep Learning ebook for free
