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Hands-On Mathematics for Deep Learning

You're reading from  Hands-On Mathematics for Deep Learning

Product type Book
Published in Jun 2020
Publisher Packt
ISBN-13 9781838647292
Pages 364 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Jay Dawani Jay Dawani
Profile icon Jay Dawani
LinkedIn
Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
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Table of Contents (19) Chapters

Preface 1. Section 1: Essential Mathematics for Deep Learning
2. Linear Algebra 3. Vector Calculus 4. Probability and Statistics 5. Optimization 6. Graph Theory 7. Section 2: Essential Neural Networks
8. Linear Neural Networks 9. Feedforward Neural Networks 10. Regularization 11. Convolutional Neural Networks 12. Recurrent Neural Networks 13. Section 3: Advanced Deep Learning Concepts Simplified
14. Attention Mechanisms 15. Generative Models 16. Transfer and Meta Learning 17. Geometric Deep Learning 18. Other Books You May Enjoy

Exploring popular ConvNet architectures

Now that we know how CNNs are built and trained, it is time to explore some of the popular architectures that are used and understand what makes them so powerful.

VGG-16

The VGG network is a derivation of AlexNet that was created by Andrew Zisserman and Karen Simonyan at the Visual Geometry Group (VGG) at the University of Oxford in 2015. This architecture is simpler than the one we saw earlier, but it gives us a much better framework to work with. VGGNet was also trained on the ImageNet dataset, except it takes images with a size of 224 × 224 × 3 that are sampled from the rescaled images in the dataset as input. You may have noticed that we have headed this section VGG-16...

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Authors (1)

Profile icon Jay Dawani
LinkedIn
Jay Dawani is a former professional swimmer turned mathematician and computer scientist. He is also a Forbes 30 Under 30 Fellow. At present, he is the Director of Artificial Intelligence at Geometric Energy Corporation (NATO CAGE) and the CEO of Lemurian Labs - a startup he founded that is developing the next generation of autonomy, intelligent process automation, and driver intelligence. Previously he has also been the technology and R&D advisor to Spacebit Capital. He has spent the last three years researching at the frontiers of AI with a focus on reinforcement learning, open-ended learning, deep learning, quantum machine learning, human-machine interaction, multi-agent and complex systems, and artificial general intelligence.
Read more
See other products by Jay Dawani

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