论文标题
与Keras深度神经网络的应用
Applications of Deep Neural Networks with Keras
论文作者
论文摘要
深度学习是一系列令人兴奋的神经网络新技术。通过高级训练技术和神经网络架构组件的结合，现在可以创建可以将表格数据，图像，文本和音频作为输入和输出来处理的神经网络。深度学习使神经网络可以像人脑的功能一样学习信息的层次结构。本课程将向学生介绍经典的神经网络结构，卷积神经网络（CNN），长期记忆（LSTM），封闭式复发性神经网络（GRU），一般对抗性网络（GAN）和增强学习。将涵盖这些体系结构在计算机视觉，时间序列，安全性，自然语言处理（NLP）和数据生成中的应用。高性能计算（HPC）方面将证明如何在图形处理单元（GPU）和网格上利用深度学习。重点是将深度学习的应用到问题上，并在数学基础上进行了一些介绍。读者将使用Python编程语言使用Google Tensorflow和Keras实施深度学习。在本书之前不必了解Python。但是，假定对至少一种编程语言的熟悉程度。
Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed.
