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Advanced Deep Learning with Python

You're reading from   Advanced Deep Learning with Python Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789956177
Length 468 pages
Edition 1st Edition
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Author (1):
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Ivan Vasilev Ivan Vasilev
Author Profile Icon Ivan Vasilev
Ivan Vasilev
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Core Concepts
2. The Nuts and Bolts of Neural Networks FREE CHAPTER 3. Section 2: Computer Vision
4. Understanding Convolutional Networks 5. Advanced Convolutional Networks 6. Object Detection and Image Segmentation 7. Generative Models 8. Section 3: Natural Language and Sequence Processing
9. Language Modeling 10. Understanding Recurrent Networks 11. Sequence-to-Sequence Models and Attention 12. Section 4: A Look to the Future
13. Emerging Neural Network Designs 14. Meta Learning 15. Deep Learning for Autonomous Vehicles 16. Other Books You May Enjoy

Introducing AlexNet

The first model we'll discuss is the winner of the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC, or simply ImageNet). It's nicknamed AlexNet (ImageNet Classification with Deep Convolutional Neural Networks, https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), after one of its authors, Alex Krizhevsky. Although this model is rarely used nowadays, it's an important milestone in contemporary deep learning.

The following diagram shows the network architecture:

The AlexNet architecture. The original model was split in two, so it can fit on the memory of two GPUs

The model has five cross-correlated convolutional layers, three overlapping max pooling layers, three fully connected layers, and ReLU activations. The output is a 1,000-way softmax (one for each ImageNet class). The first...

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