CNNs present a promising future for computer vision. CNNs have laid out a benchmark for complex computer vision tasks such as detection and recognition with their remarkable performance in the ILSVRC competition over consecutive years. But the computation power required by these CNN models has always been quite high. This could lead to a major setback for the commercial use of CNNs. Almost all object detection-related tasks in the real world are performed through portable devices, such as mobile phones, surveillance cameras, or any other embedded device. These devices have limited computational abilities and memory. To make any deep learning network running on a portable device, the network weights and the number of calculations occurring in the network (that is, the number of parameters in the network) have to very small. CNNs have millions of...
Hands-On Deep Learning Architectures with Python
By :
Hands-On Deep Learning Architectures with Python
By:
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
Other Books You May Enjoy
How would like to rate this book
Customer Reviews
