VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million imag

Exploring an advanced state of the art deep learning models and its applications using Popular python libraries like Keras, Tensorflow, and Pytorch Key Features • A strong foundation on neural networks and deep learning with Python libraries. • Explore advanced deep learning techniques and their applications across computer vision and NLP. • Learn how a computer can navigate in complex environments with reinforcement learning. Book Description With the surge of Artificial Intelligence in each and every application catering to both business and consumer needs, Deep Learning becomes the prime need of today and future market demands. This book explores deep learning and builds a strong deep learning mindset in order to put them into use in their smart artificial intelligence projects. This second edition builds strong grounds of deep learning, deep neural networks and how to train them with high-performance algorithms and popular python frameworks. You will uncover different neural networks architectures like convolutional networks, recurrent networks, long short term memory (LSTM) and solve problems across image recognition, natural language processing, and time-series prediction. You will also explore the newly evolved area of reinforcement learning and it will help you to understand the state-of-the-art algorithms which are the main engines behind popular game Go, Atari, and Dota. By the end of the book, you will be well versed with practical deep learning knowledge and its real-world applications What you will learn • Grasp mathematical theory behind neural networks and deep learning process. • Investigate and resolve computer vision challenges using convolutional networks and capsule networks. • Solve Generative tasks using Variational Autoencoders and Generative Adversarial Nets (GANs). • Explore Reinforcement Learning and understand how agents behave in a complex environment. • Implement complex natural language processing tasks using recurrent networks (LSTM

Neural Network Design Demonstrations(神经网络设计代码演示) 一个神经网络源程序教学任务包，里面有130个M文件，可直接调用，供大家参考。 Neural Networks: A Classroom Approach Satish kumar

当代最经典的自适应神经网络控制书 由IEEE Fellow Shuzhi Sam Ge 编写

一部值得精读的著作,将神经网络方法和自然语言处理的相关课题紧密的联合了起来.介绍了神经网络的构建细节和机器学习的一些基本内容,并且包含了RNN,CNN等主流神经网络在NLP中的应用实例,另外2017年新书,有最新的学术信息.

神经网络设计第二版pdf, Neural Network Design (2nd Edition)英语原版，2014年出版，Martin T. Hagan Oklahoma State University Stillwater, Oklahoma Howard B. Demuth University of Colorado Boulder, Colorado Mark Hudson Beale MHB Inc. Hayden, Idaho Orlando De Jesús Consultant Frisco, Texas

Hopfield Neural Network——Hopfield神经网络的python代码，基于Language: Python 3.5.x，API: Google TensorFlow 1.0.x。实验案例有训练Hopfield网络对MNIST数字进行分类等。

Make Your Own Neural Network---------mobi 、epub、azw3

神经网路机器人机构及非线性系统控制，介绍了控制器的设计思路与稳定性证明，还是很详细的，推荐一下

Neural Network Design (2nd Edition)为大牛Martin T. Hagan巨作，深入浅出地讲解了深度学习的所有算法原理，非常适合全面深入了解人工神经网络及深度学习