diff --git a/new/css/home.css b/new/css/home.css index 7f7de625537b76f5bba4f8481c2e3bb155520445..0e16f534b81cbf0ac2cec619395634aed577e318 100755 --- a/new/css/home.css +++ b/new/css/home.css @@ -2731,12 +2731,18 @@ header.site-header .banner .github-counter > span:nth-child(1) .fa { vertical-align: middle; } .footer-nav .tr-code > img { - margin-right: 5px; + margin-bottom: 0px; + margin-top: 0px; +} +.footer-nav .tr-code > p { + line-height: 40px; + margin-top: 0px; + margin-bottom: 0px; } .footer-nav .contact-us { color: #fff; text-align: center; - line-height: 80px; + line-height: 70px; font-size: 14px; border-bottom: 1px solid #484e5e; margin-bottom: 30px; diff --git a/new/images/._email-pic.png b/new/images/._email-pic.png new file mode 100755 index 0000000000000000000000000000000000000000..1a65bfbb3a935f5fbf562363d31f4c9ba5c2e06e Binary files /dev/null and b/new/images/._email-pic.png differ diff --git a/new/images/email-pic.png b/new/images/email-pic.png new file mode 100755 index 0000000000000000000000000000000000000000..0b03f4c386d7f1e0e79684e0bd0446f1d6275767 Binary files /dev/null and b/new/images/email-pic.png differ diff --git a/new/index.html b/new/index.html index d2f4a8b74c7a7561e02077758ca69d5a3f1fc649..395c09b9b8dced74e42c7d9244f7e6713a97457c 100755 --- a/new/index.html +++ b/new/index.html @@ -1 +1 @@ -
The convoluted neural network can identify the main object in the image and output the classification result
Using the LSTM network to analyze the positive and negative aspects of the commenter's emotions from IMDB film review
Analyze user characteristics, movie features, rating scores, predict new users' ratings for different movies
Provids an intuitive and flexible interface for loading data and specifying model structure.
Supports CNN, RNN and other neural network. Easy to configure complex models.
Efficient optimization of computing, memory, communications and architecture.
Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.
The convoluted neural network can identify the main object in the image and output the classification result
Using the LSTM network to analyze the positive and negative aspects of the commenter's emotions from IMDB film review
Analyze user characteristics, movie features, rating scores, predict new users' ratings for different movies
Provids an intuitive and flexible interface for loading data and specifying model structure.
Supports CNN, RNN and other neural network. Easy to configure complex models.
Efficient optimization of computing, memory, communications and architecture.
Easy to use many CPUs/GPUs and machines to speed up your training and handle large-scale data easily.
为用户提供了直观、灵活的数据接口和模型配置接口
支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型
在计算、存储、通信、架构等方面都做了高效优化,充分发挥各种资源的性能
全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求
为用户提供了直观、灵活的数据接口和模型配置接口
支持CNN、RNN等多种神经网络结构和优化算法。简单书写配置文件即可实现复杂模型
在计算、存储、通信、架构等方面都做了高效优化,充分发挥各种资源的性能
全面支持多核、多GPU、多机环境。轻松应对大规模数据训练需求