Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.
Companies such as Google, Microsoft, and Faceb
It was great once again encounter calculus, vectors, transforms and matrices, long after school and college days. I can’t say I understood them with the same rigor as when in school though. Reading this book didn’t help me understand Neural Networks all that much as it made me familiar wi ...more
Somehow, up to Convolutional Neural Networks (~%50 of the book), there is a very good overview of what Gradient Descent is and how to impleme ...more
- Gives a really good overview of computer vision history and why traditional machine learning methods don't perform as good as convolutional networks
- The section that talks about Gradient Descent is really well explained and destroy some myths around gradient descent (even though there is no math)
- Gives a clear and intuitive idea of how convolutional layers can capture patterns in images
- It includes attention methods for NLP
- Lacks math and precise definitions (but that ...more
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