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Deep Learning for Beginners

You're reading from   Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python

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Product type Paperback
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Length 432 pages
Edition 1st Edition
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Authors (2):
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Pablo Rivas Pablo Rivas
Author Profile Icon Pablo Rivas
Pablo Rivas
Dr. Pablo Rivas Dr. Pablo Rivas
Author Profile Icon Dr. Pablo Rivas
Dr. Pablo Rivas
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Getting Up to Speed
2. Introduction to Machine Learning FREE CHAPTER 3. Setup and Introduction to Deep Learning Frameworks 4. Preparing Data 5. Learning from Data 6. Training a Single Neuron 7. Training Multiple Layers of Neurons 8. Section 2: Unsupervised Deep Learning
9. Autoencoders 10. Deep Autoencoders 11. Variational Autoencoders 12. Restricted Boltzmann Machines 13. Section 3: Supervised Deep Learning
14. Deep and Wide Neural Networks 15. Convolutional Neural Networks 16. Recurrent Neural Networks 17. Generative Adversarial Networks 18. Final Remarks on the Future of Deep Learning 19. Other Books You May Enjoy

References

  • Abadi, M., and Andersen, D. G. (2016). Learning to protect communications with adversarial neural cryptography. arXiv preprint arXiv:1610.06918.
  • Ilyas, A., Engstrom, L., Athalye, A., and Lin, J. (2018). Black box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598.
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).
  • Sukhbaatar, S., and Fergus, R. (2016). Learning multi-agent communication with backpropagation. In Advances in neural information processing systems (pp. 2244-2252).
  • Rivas, P., and Banerjee, P. (2020). Neural-Based Adversarial Encryption of Images in ECB Mode with 16-bit Blocks. In International Conference on Artificial Intelligence.
  • Cohen, J. M., Rosenfeld, E., and Kolter, J. Z. (2019). Certified adversarial robustness via randomized smoothing. arXiv preprint arXiv:1902.02918...
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