We began this chapter by discussing the need for mobile neural networks to make CNNs work in real-time applications. We discussed the two benchmark MobileNet architectures that were introduced by Google—MobileNet and MobileNetV2. We looked at how modifications such as depth-wise separable convolution work and replaced the standard convolutions, enabling the network to achieve the same results with significantly fewer parameters. With MobileNetV2, we looked at the possibility of reducing the network even further with expansion layers and bottleneck layers. We also looked at the implementation of both the networks in Keras and compared both the networks in terms of the number of parameters, MACs, and memory required. Finally, we discussed the successful combination of MobileNets with object detection networks, such as SSD, to achieve object detection on mobile devices...
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Hands-On Deep Learning Architectures with Python
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Hands-On Deep Learning Architectures with Python
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Overview of this book
Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.
Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.
By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.
Table of Contents (15 chapters)
Preface
Getting Started with Deep Learning
Deep Feedforward Networks
Restricted Boltzmann Machines and Autoencoders
Section 2: Convolutional Neural Networks
CNN Architecture
Mobile Neural Networks and CNNs
Section 3: Sequence Modeling
Recurrent Neural Networks
Section 4: Generative Adversarial Networks (GANs)
Generative Adversarial Networks
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
New Trends of Deep Learning
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