Milvus: Open-Source Vector Database for AI Applications培训

H2O AutoML is an artificial intelligence platform that automates the process of building, selecting and optimizing large numbers of machine learning models.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automate the process of building and selecting the best machine learning algorithm and parameters.

By the end of this training, participants will be able to:

• Automate the machine learning workflow.
• Automatically train and tune many machine learning models within a specified time range.
• Train stacked ensembles to arrive at highly predictive ensemble models.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Auto-sklearn is a Python package built around the scikit-learn machine learning library. It automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its parameters.

This instructor-led, live training (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

• Automate the process of training highly efficient machine learning models.
• Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
• Use the power of machine learning to solve real-world business problems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Auto-Keras (Also known as Autokeras or Auto Keras) is an open source Python library for automated machine learning (AutoML).

This instructor-led, live training (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model.

By the end of this training, participants will be able to:

• Automate the process of training highly efficient machine learning models.
• Automatically search for the best parameters for deep learning models.
• Build highly accurate machine learning models.
• Use the power of machine learning to solve real-world business problems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.
• To learn more about Auto-Keras, please visit: https://autokeras.com/

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

By the end of this training, participants will be able to:

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange

Stable Diffusion is a powerful deep learning model that can generate detailed images based on text descriptions. 

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation.

By the end of this training, participants will be able to:

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange

这种以讲师为主导的中国 现场现场培训针对的是希望了解AlphaFold工作原理并使用AlphaFold模型作为实验研究指南的生物学家。

在培训结束时，参与者将能够：

• 了解 AlphaFold 的基本原理。
• 了解 AlphaFold 的工作原理。
• 了解如何解释 AlphaFold 预测和结果。

这种以讲师为主导的中国现场现场培训面向希望使用TensorFlow Lite在嵌入式设备上部署深度学习模型的开发人员。

在培训结束时，参与者将能够：

• 在嵌入式设备上安装和配置 Tensorflow Lite。
• 了解 TensorFlow Lite 的基本概念和组件。
• 将现有模型转换为 TensorFlow Lite 格式，以便在嵌入式设备上执行。
• 在小型设备和 TensorFlow Lite 的限制范围内工作，同时学习如何扩展可以运行的操作范围。
• 在运行 Linux 的嵌入式设备上部署深度学习模型。

这种以讲师为主导的中国现场培训（现场或远程）针对希望使用TensorFlow Lite开发具有深度学习功能的移动应用程序的开发人员。

在培训结束时，参与者将能够：

• 安装和配置 TensorFlow Lite。
• 了解 TensorFlow、机器学习和深度学习背后的原理。
• 将 TensorFlow 模型加载到 Android 设备上。
• 在移动应用程序中启用深度学习和机器学习功能，例如计算机视觉和自然语言识别。

这种以讲师为主导的中国现场培训（现场或远程）面向希望在非常小的嵌入式设备上编写，加载和运行机器学习模型的工程师。

在培训结束时，参与者将能够：

• 安装 TensorFlow Lite。
• 将机器学习模型加载到嵌入式设备上，使其能够检测语音、对图像进行分类等。
• 将 AI 添加到硬件设备，而无需依赖网络连接。

这种以讲师为主导的现场培训（现场或远程）面向希望使用TensorFlow Lite开发具有深度学习功能的iOS移动应用程序的开发人员。

在培训结束时，参与者将能够：

• 安装和配置 TensorFlow Lite。
• 了解移动设备上的 TensorFlow 和机器学习背后的原理。
• 将 TensorFlow 模型加载到 iOS 设备上。
• 运行能够检测和分类通过设备摄像头捕获的对象的 iOS 应用程序。

这种以讲师为主导的中国现场现场培训针对的是希望使用Chainer在Python中构建和训练神经网络的研究人员和开发人员，同时使代码易于调试。

在培训结束时，参与者将能够：

• 设置必要的开发环境以开始开发神经网络模型。
• 使用易于理解的源代码定义和实现神经网络模型。
• 执行示例并修改现有算法以优化深度学习训练模型，同时利用 GPU 实现高性能。

这种由讲师指导的中国现场（现场或远程）现场培训面向希望使用Horovod运行分布式深度学习训练并将其扩展为在多个GPU上并行运行的开发人员或数据科学家。

在培训结束时，参与者将能够：

• 设置必要的开发环境以开始运行深度学习训练。
• 安装和配置 Horovod 以使用 TensorFlow、Keras、PyTorch 和 Apache MXNet 训练模型。
• 使用 Horovod 扩展深度学习训练，使其在多个 GPU 上运行。

这种以讲师为主导的中国现场现场培训面向希望加速实时机器学习应用程序并大规模部署的数据科学家。

在培训结束时，参与者将能够：

• 安装 OpenVINO 工具包。
• 使用 FPGA 加速计算机视觉应用。
• 在FPGA上执行不同的CNN层。
• 跨 Kubernetes 集群中的多个节点扩展应用程序。

这种以讲师为主导的现场培训（现场或远程）针对的是希望使用Apache MXNet来构建和部署用于图像识别的深度学习模型的数据科学家。

在培训结束时，参与者将能够：

• 安装和配置 Apache MXNet 及其组件。
• 了解MXNet的架构和数据结构。
• 使用 Apache MXNet 的低级和高级 API 高效构建神经网络。
• 构建用于图像分类的卷积神经网络。

这种以讲师为主导的中国现场现场培训针对的是希望将深度学习模型应用于图像识别应用的技术人员。

在培训结束时，参与者将能够：

• 安装和配置 Keras。
• 快速构建深度学习模型原型。
• 实现卷积网络。
• 实施循环网络。
• 在 CPU 和 GPU 上执行深度学习模型。