keras的数据集 from keras.datasets import imdb (x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=20000)

利用keras实现u-net这样一个全卷积神经网络，进行图像分割

vgg16_weights_tf_dim_ordering_tf_kernels_notop,Keras VGG16

resnet50_weights_tf_dim_ordering_tf_kernels_notop Linux下是放在“~/.keras/models/”中 Win下则放在Python的“settings/.keras/models/”中 Windows-weights路径：C:\Users\你的用户名\.keras\models anaconda下依然好用

10行Python代码实现目标检测,内含文档和安装实现教程，方便大家参考学习

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use Scikit-Learn to track an example machine-learning project end-to-end * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets

数据集为Keras自带的IMDB数据集，运行文件是自动下载。后台为TensorFlow，文件为.py格式，打开即可直接运行。

有时候keras数据集特别难下载。这里我把我下载好的数据集拿出来和大家共享，希望能帮助到大家。。。

MNSIT数据集、mnist.pkl.gz。 为两种keras加载mnist数据集方法用到的数据 https://blog.csdn.net/shaozhulei555/article/details/76144903

基于keras深度学习框架，应用卷积神经网络CNN实现cifar-10图片分类