In Learning Transferable Architectures for Scalable Image Recognition, Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le, 2017 https://arxiv.org/abs/1707.07012. propose to learn an architectural building block on a small dataset that can be transferred to a large dataset. The authors propose to search for the best convolutional layer (or cell) on the CIFAR-10 dataset and then apply this learned cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters. Precisely, all convolutional networks are made of convolutional layers (or cells) with identical structures but different weights. Searching for the best convolutional architectures is therefore reduced to searching for the best cell structures, which is faster more likely to generalize to other problems. Although the cell is not learned directly on ImageNet...
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Table Of Contents
TensorFlow 1.x Deep Learning Cookbook
By :
TensorFlow 1.x Deep Learning Cookbook
By:
Overview of this book
Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.
In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on
some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.
By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,
GANs, and autoencoders.
Table of Contents (15 chapters)
Preface
Free Chapter
TensorFlow - An Introduction
Regression
Neural Networks - Perceptron
Convolutional Neural Networks
Advanced Convolutional Neural Networks
Recurrent Neural Networks
Unsupervised Learning
Autoencoders
Reinforcement Learning
Mobile Computation
Generative Models and CapsNet
Distributed TensorFlow and Cloud Deep Learning
Learning to Learn with AutoML (Meta-Learning)
TensorFlow Processing Units
