课程大纲
介绍
Keras 和深度学习框架
- TensorFlow 和 Theano 后端
- Keras 与 Tensorflow
数据和 Machine Learning
- 表格数据、可视化数据、非结构化数据等
- 无监督学习、监督学习、强化学习等
准备开发环境
- 安装和配置 Anaconda
- 使用 TensorFlow 后端安装 Keras
Neural Networks 在Keras中
- 使用 Keras 函数式 API 构建网络
- 预处理和拟合数据
- 定义 Keras 模型
多个输入和输出网络
- 构建两个输入网络
- 表示高基数数据
- 合并图层
- 扩展两个输入网络
- 构建具有多个输出的神经网络
- 同时解决多个问题
培训和预培训
- 训练模型
- 保存和加载模型
- 在模型上使用 ResNet50
张量板
- 导出 Keras 日志
- 可视化计算图和训练进度
Google 云
- 导出模型
- 上传 Keras 个模型
- 在 Google Cloud 中使用模型
总结和结论
要求
- 对基本线性代数的理解
观众
- 软件工程师
客户评论 (4)
培训师非常乐意回答我所做的所有问题
Caterina - Stamtech
课程 - Developing APIs with Python and FastAPI
机器翻译
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
课程 - Build REST APIs with Python and Flask
传授培训师的实践知识和经验。
Rumel Mateusz - Pojazdy Szynowe PESA Bydgoszcz SA
课程 - GUI Programming with Python and PyQt
机器翻译
As I was the only participant the training could be adapted to my needs.