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Advanced Deep Learning with Python

Advanced Deep Learning with Python

By : Vasilev
4.8 (23)
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Advanced Deep Learning with Python

Advanced Deep Learning with Python

4.8 (23)
By: Vasilev

Overview of this book

In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
Table of Contents (17 chapters)
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1
Section 1: Core Concepts
3
Section 2: Computer Vision
8
Section 3: Natural Language and Sequence Processing
12
Section 4: A Look to the Future

Understanding Inception networks

Inception networks (Going Deeper with Convolutions, https://arxiv.org/abs/1409.4842) were introduced in 2014, when they won the ImageNet challenge of that year (there seems to be a pattern here). Since then, the authors have released multiple improvements (versions) of the architecture.

Fun fact: the name Inception comes in part from the We need to go deeper internet meme, related to the movie Inception.

The idea behind Inception networks started from the basic premise that the objects in an image have different scales. A distant object might take up a small region of the image, but the same object, once nearer, might take up the majority of the image. This presents a difficulty for standard CNNs, where the neurons in the different layers have a fixed receptive field size as imposed on the input image. A regular network might be a good detector...

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