由讲师进行实时指导的TensorFlow本地培训课程通过互动讨论和动手实操演示了如何使用TensorFlow系统促进机器学习研究，并使其从研究原型到生产系统的转换变得快速和轻松。
TensorFlow培训形式包括“现场实时培训”和“远程实时培训”。现场实时培训可在客户位于中国的所在场所或NobleProg位于中国的企业培训中心进行，远程实时培训可通过交互式远程桌面进行。
NobleProg -- 您的本地培训提供商
客户评论
人间识别和电路板坏点检测
王 春柱 - 中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
演示
中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
About face area.
中移物联网
课程: Deep Learning for NLP (Natural Language Processing)
我真的很感激克里斯对我们问题的明确答案。
Léo Dubus
课程: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
Machine Translated
我一般都很喜欢知识渊博的教练。
Sridhar Voorakkara
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我对这门课程的标准感到惊讶 - 我会说它是大学标准。
David Relihan
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
非常好的全面概述。 Go OD背景到原因Tensorflow工作，因为它确实。
Kieran Conboy
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我喜欢有机会提出问题并对理论进行更深入的解释。
Sharon Ruane
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
非常更新的方法或CPI（张量流，时代，学习）做机器学习。
Paul Lee
课程: TensorFlow for Image Recognition
Machine Translated
鉴于技术前景：未来哪种技术/流程可能变得更加重要;看，这项技术可以用于什么。
Commerzbank AG
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
我从主题选择中受益。训练风格。练习方向。
Commerzbank AG
课程: Neural Networks Fundamentals using TensorFlow as Example
Machine Translated
涵盖广泛的主题和领导者的实质性知识。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
缺乏
ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
讲师的大量理论和实践知识。培训师的沟通能力。在课程中，您可以提出问题并获得满意的答案。
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
实用部分，我们实现了算法。这样可以更好地理解该主题。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
练习和实施的例子
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
讨论的例子和问题。
ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
实质性知识，承诺，热情的知识转移方式。理论讲座后的实例。
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
Maciej先生准备的实践练习
ING Bank Śląski S.A.; Kamil Kurek Programowanie
课程: Understanding Deep Neural Networks
Machine Translated
很多实用技巧
Pawel Dawidowski - ABB Sp. z o.o.
课程: Deep Learning with TensorFlow
Machine Translated
许多与解决方案实施相关的信息
Michał Smolana - ABB Sp. z o.o.
课程: Deep Learning with TensorFlow
Machine Translated
来自各种AI / IT / SQL / IoT问题的讲师的大量实用技巧和知识。
ABB Sp. z o.o.
课程: Deep Learning with TensorFlow
Machine Translated
我从零知识开始，到最后，我得以建立和训练自己的网络。
Huawei Technologies Duesseldorf GmbH
课程: TensorFlow for Image Recognition
Machine Translated
托马斯对信息非常了解，课程节奏很快。
Raju Krishnamurthy - Google
课程: TensorFlow Extended (TFX)
Machine Translated
TensorFlow课程大纲
课程名称
课程时长
课程概览
课程名称
课程时长
课程概览
21小时
课程概览
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow and machine learning on mobile devices.
- Load TensorFlow Models onto an iOS device.
- Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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.
35小时
课程概览
TensorFlow ™是一个使用数据流图进行数值计算的开源软件库。
SyntaxNet是TensorFlow的神经网络自然语言处理框架。
Word 2Vec用于学习单词的矢量表示，称为“单词嵌入”。 Word 2vec是一种特别计算有效的预测模型，用于学习原始文本中的单词嵌入。它有两种形式，连续Bag-of- Word模型（CBOW）和Skip-Gram模型（Mikolov等人的第3.1和3.2章）。
使用串联，SyntaxNet和Word 2Vec允许用户从自然语言输入生成学习嵌入模型。
听众
本课程面向打算在TensorFlow图中使用SyntaxNet和Word 2Vec模型的开发人员和工程师。
完成本课程后，代表们将：
- 了解TensorFlow的结构和部署机制
- 能够执行安装/生产环境/架构任务和配置
- 能够评估代码质量，执行调试，监控
- 能够实现高级生产，如培训模型，嵌入术语，构建图形和记录
SyntaxNet是TensorFlow的神经网络自然语言处理框架。
Word 2Vec用于学习单词的矢量表示，称为“单词嵌入”。 Word 2vec是一种特别计算有效的预测模型，用于学习原始文本中的单词嵌入。它有两种形式，连续Bag-of- Word模型（CBOW）和Skip-Gram模型（Mikolov等人的第3.1和3.2章）。
使用串联，SyntaxNet和Word 2Vec允许用户从自然语言输入生成学习嵌入模型。
听众
本课程面向打算在TensorFlow图中使用SyntaxNet和Word 2Vec模型的开发人员和工程师。
完成本课程后，代表们将：
- 了解TensorFlow的结构和部署机制
- 能够执行安装/生产环境/架构任务和配置
- 能够评估代码质量，执行调试，监控
- 能够实现高级生产，如培训模型，嵌入术语，构建图形和记录
21小时
课程概览
TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.
By the end of this training, participants will be able to:
- Install and configure TFX and supporting third-party tools.
- Use TFX to create and manage a complete ML production pipeline.
- Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
- Deploy machine learning features to web applications, mobile applications, IoT devices and more.
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.
28小时
课程概览
本课程通过具体的例子探讨了Tensor Flow在图像识别方面的应用
听众
本课程适用于寻求将TensorFlow用于图像识别的工程师
完成本课程后，代表们将能够：
- 了解TensorFlow的结构和部署机制
- 执行安装/生产环境/架构任务和配置
- 评估代码质量，执行调试，监控
- 实施先进的生产，如培训模型，建立图表和记录
听众
本课程适用于寻求将TensorFlow用于图像识别的工程师
完成本课程后，代表们将能够：
- 了解TensorFlow的结构和部署机制
- 执行安装/生产环境/架构任务和配置
- 评估代码质量，执行调试，监控
- 实施先进的生产，如培训模型，建立图表和记录
21小时
课程概览
TensorFlow是Go ogle Deep Learning开源软件库的第二代API。该系统旨在促进机器学习的研究，并使其从研究原型到生产系统的快速和轻松过渡。
听众
本课程面向希望将TensorFlow用于Deep Learning项目的工程师
完成本课程后，代表们将：
- 了解TensorFlow的结构和部署机制
- 能够执行安装/生产环境/架构任务和配置
- 能够评估代码质量，执行调试，监控
- 能够实现高级生产，如培训模型，构建图形和记录
听众
本课程面向希望将TensorFlow用于Deep Learning项目的工程师
完成本课程后，代表们将：
- 了解TensorFlow的结构和部署机制
- 能够执行安装/生产环境/架构任务和配置
- 能够评估代码质量，执行调试，监控
- 能够实现高级生产，如培训模型，构建图形和记录
7小时
课程概览
TensorFlow Serving是一个为机器学习（ML）模型提供服务的系统。 在这个有指导的现场培训中，参与者将学习如何配置和使用TensorFlow Serving在生产环境中部署和管理ML模型。 在培训结束后，参与者将能够： 培训，出口和服务各种TensorFlow模型使用单一架构和一组API来测试和部署算法扩展TensorFlow服务于TensorFlow型号之外的其他类型的模型 听众 开发商数据科学家 课程的格式 部分讲座，部分讨论，练习和沉重的练习
21小时
课程概览
TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
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.
This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
- Install TensorFlow Lite.
- Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
- Add AI to hardware devices without relying on network connectivity.
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.
21小时
课程概览
TensorFlow Lite is an open source deep learning framework for mobile devices and embedded systems.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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 TensorFlow, please visit: https://www.tensorflow.org/lite/
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities.
By the end of this training, participants will be able to:
- Install and configure TensorFlow Lite.
- Understand the principles behind TensorFlow, machine learning and deep learning.
- Load TensorFlow Models onto an Android device.
- Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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 TensorFlow, please visit: https://www.tensorflow.org/lite/
28小时
课程概览
NLP的深度学习使机器学习简单到复杂的语言处理。当前可能的任务包括语言翻译和照片的标题生成。 DL（深度学习）是ML（机器学习）的子集。 Python是一种流行的编程语言，包含用于NLP深度学习的库。 在这个有指导意义的现场培训中，参与者将学习使用Python库进行NLP（自然语言处理），因为他们创建了一个处理一组图片并生成字幕的应用程序。 在培训结束后，参与者将能够： 使用Python库设计和编写用于NLP的DL 创建读取大量图片并生成关键字的Python代码创建Python代码，从检测到的关键字中生成字幕 听众 对语言学感兴趣的程序员希望了解NLP（自然语言处理）的程序员 课程的格式 部分讲座，部分讨论，练习和沉重的练习
21小时
课程概览
TensorFlow Lite is an open source deep learning framework for executing models on mobile and embedded devices with limited compute and memory resources.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices.
By the end of this training, participants will be able to:
- Install and configure Tensorflow Lite on an embedded device.
- Understand the concepts and components underlying TensorFlow Lite.
- Convert existing machine learning models to TensorFlow Lite format for execution on embedded devices.
- Work within the limitations of small devices and TensorFlow Lite, while learning how to expand their default capabilities.
- Deploy deep learning models on embedded devices running Linux to solve physical world problems such as recognizing images and voice, predicting patterns, and initiating movements and responses from robots and other embedded systems in the field.
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.
14小时
课程概览
TensorFlow.js is a JavaScript framework for machine learning. TensorFlow.js enables users to build and train machine learning models directly in JavaScript.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow.js to identify patterns and generate predictions through machine learning models.
By the end of this training, participants will be able to:
- Build and train machine learning models with TensorFlow.js.
- Run machine learning models in the browser or under Node.js.
- Retrain pre-existing machine learning models using custom data.
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.
21小时
课程概览
TensorFlow是开发的流行和机器学习库Go眄深学习，数值计算和大规模机器学习。 TensorFlow 2.0于2019年1月发布，是TensorFlow的最新版本，包括对急切执行，兼容性和API一致性的改进。
这种以讲师为主导的现场培训（现场或远程）针对希望使用Tensorflow 2.0构建预测器，分类器，生成模型，神经网络等的开发人员和数据科学家。
在培训结束时，参与者将能够：
- 安装和配置TensorFlow 2.0。
- 了解TensorFlow 2.0与以前版本相比的优势。
- 建立深度学习模型。
- 实现高级图像分类器。
- 将深度学习模型部署到云，移动和物联网设备。
课程格式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在实时实验室环境中亲自实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们安排。
- 要了解有关TensorFlow更多信息，请访问：https：//www.tensorflow.org/
这种以讲师为主导的现场培训（现场或远程）针对希望使用Tensorflow 2.0构建预测器，分类器，生成模型，神经网络等的开发人员和数据科学家。
在培训结束时，参与者将能够：
- 安装和配置TensorFlow 2.0。
- 了解TensorFlow 2.0与以前版本相比的优势。
- 建立深度学习模型。
- 实现高级图像分类器。
- 将深度学习模型部署到云，移动和物联网设备。
课程格式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在实时实验室环境中亲自实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们安排。
- 要了解有关TensorFlow更多信息，请访问：https：//www.tensorflow.org/
28小时
课程概览
本课程将为您提供神经网络知识，通常用于机器学习算法，深度学习（算法和应用程序）。
这种训练更注重基本面，但会帮助你选择合适的技术： TensorFlow ， Caffe ，泰亚诺，DeepDrive， Keras ，等等这些例子中所作TensorFlow 。
这种训练更注重基本面，但会帮助你选择合适的技术： TensorFlow ， Caffe ，泰亚诺，DeepDrive， Keras ，等等这些例子中所作TensorFlow 。
14小时
课程概览
TensorFlow is an open source machine learning library. TensorFlow provides users the ability to use and create artificial intelligence for detecting and predicting fraud.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
- Create a fraud detection model in Python and TensorFlow.
- Build linear regressions and linear regression models to predict fraud.
- Develop an end-to-end AI application for analyzing fraud data.
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.
This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
- Create a fraud detection model in Python and TensorFlow.
- Build linear regressions and linear regression models to predict fraud.
- Develop an end-to-end AI application for analyzing fraud data.
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.
14小时
课程概览
嵌入式投影仪是一款开源Web应用程序，用于可视化用于训练机器学习系统的数据。由Google创建，它是TensorFlow的一部分。 这个有指导意义的现场培训介绍了嵌入式投影仪背后的概念，并让参与者通过演示项目的设置。 在培训结束后，参与者将能够： 探索机器学习模型如何解释数据浏览数据的3D和2D视图以了解机器学习算法如何解释它理解嵌入背后的概念及其在表示图像，单词和数字的数学向量中的作用。 探索特定嵌入的属性以了解模型的行为将嵌入项目应用于真实世界的用例，例如为音乐爱好者建立歌曲推荐系统 听众 开发商数据科学家 课程的格式 部分讲座，部分讨论，练习和沉重的练习
35小时
课程概览
本课程首先介绍神经网络的概念知识，通常还包括机器学习算法，深度学习（算法和应用程序）。
本次培训的一部分-1（40％）更注重基本面，但会帮助你选择合适的技术： TensorFlow ， Caffe ，Theano，DeepDrive， Keras等。
本次培训的第2部分（20％）介绍了Theano--一个python库，可以轻松编写深度学习模型。
第3部分（40％）的培训将广泛基于Tensorflow - Go ogle的Deep Learning开源软件库的第二代API。示例和动手都将在TensorFlow 。
听众
本课程面向希望将TensorFlow用于Deep Learning项目的工程师
完成本课程后，代表们将：
-
对深度神经网络（DNN），CNN和RNN有很好的理解
-
了解TensorFlow的结构和部署机制
-
能够执行安装/生产环境/架构任务和配置
-
能够评估代码质量，执行调试，监控
-
能够实现高级生产，如培训模型，构建图形和记录
本次培训的一部分-1（40％）更注重基本面，但会帮助你选择合适的技术： TensorFlow ， Caffe ，Theano，DeepDrive， Keras等。
本次培训的第2部分（20％）介绍了Theano--一个python库，可以轻松编写深度学习模型。
第3部分（40％）的培训将广泛基于Tensorflow - Go ogle的Deep Learning开源软件库的第二代API。示例和动手都将在TensorFlow 。
听众
本课程面向希望将TensorFlow用于Deep Learning项目的工程师
完成本课程后，代表们将：
-
对深度神经网络（DNN），CNN和RNN有很好的理解
-
了解TensorFlow的结构和部署机制
-
能够执行安装/生产环境/架构任务和配置
-
能够评估代码质量，执行调试，监控
-
能够实现高级生产，如培训模型，构建图形和记录
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