TensorFlow培训
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China的所在场所或NobleProg位于China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
有知识的教练。
Sridhar Voorakkara
课程名称: Neural Networks Fundamentals using TensorFlow as Example
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我对这个班级的标准感到惊讶，我会说这是大学标准。 。
David Relihan
课程名称: Neural Networks Fundamentals using TensorFlow as Example
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非常好的全面的概述。良好的背景为什么Tensorflow运作，因为它
Kieran Conboy
课程名称: Neural Networks Fundamentals using TensorFlow as Example
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我喜欢提问的机会，并更深入地解释理论。 。
Sharon Ruane
课程名称: Neural Networks Fundamentals using TensorFlow as Example
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非常新的方法或api（tensorflow，kera，tflearn）来做机器学习。
Paul Lee
课程名称: TensorFlow for Image Recognition
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鉴于技术前景：未来什么技术/流程可能变得更加重要;看，这项技术可以用于什么。
Commerzbank AG
课程名称: Neural Networks Fundamentals using TensorFlow as Example
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主题选择。培训风格。实践导向。
Commerzbank AG
课程名称: Neural Networks Fundamentals using TensorFlow as Example
Translated by
主题选择。培训风格。实践导向。
Commerzbank AG
课程名称: Neural Networks Fundamentals using TensorFlow as Example
Translated by
论文讲解
课程名称: Deep Learning with TensorFlow - bespoke
课程简介
| 代码 | 名字 | 时长 | 概览 |
|---|---|---|---|
| tf101 | Deep Learning with TensorFlow | 21小时 | TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging |
| tfir | TensorFlow for Image Recognition | 28小时 | This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition Audience This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition After completing this course, delegates will be able to: understand TensorFlow’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, building graphs and logging |
| dlv | Deep Learning for Vision | 21小时 | Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images This course provide working examples. |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 28小时 | This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| datamodeling | Pattern Recognition | 35小时 | This course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired. Audience Data analysts PhD students, researchers and practitioners |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7小时 | The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications. By the end of the training, participants will be able to: Train various types of neural networks on large amounts of data Use TPUs to speed up the inference process by up to two orders of magnitude Utilize TPUs to process intensive applications such as image search, cloud vision and photos Audience Developers Researchers Engineers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | 14小时 | Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow. This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project. By the end of this training, participants will be able to: Explore how data is being interpreted by machine learning models Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals. Explore the properties of a specific embedding to understand the behavior of a model Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| tensorflowserving | TensorFlow Serving | 7小时 | TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment. By the end of this training, participants will be able to: Train, export and serve various TensorFlow models Test and deploy algorithms using a single architecture and set of APIs Extend TensorFlow Serving to serve other types of models beyond TensorFlow models Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| mlbankingpython_ | 机器学习用于银行业务（使用Python） | 21小时 | 在这一由讲师引导的现场培训中，参与者将学习如何应用机器学习技术和工具来解决银行业的现实问题。Python将被用作编程语言。 参与者首先学习关键原则，然后通过建立自己的机器学习模型并使用模型来完成一些现场项目以将所学知识运用到实践中。 受众 开发人员 数据科学家 课程形式 部分讲座、部分讨论、练习和大量实操 |
| undnn | Understanding Deep Neural Networks | 35小时 | This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications). Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc. Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy. Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: have a good understanding on deep neural networks(DNN), CNN and RNN understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject. The Duration of the complete course will be around 70 hours and not 35 hours. |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28小时 | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to: Design and code DL for NLP using Python libraries Create Python code that reads a substantially huge collection of pictures and generates keywords Create Python Code that generates captions from the detected keywords Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing) Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
Upcoming Courses
| 课程 | 日期 | 价格【远程 / 传统课堂】 |
|---|---|---|
| Understanding Deep Neural Networks - 上海 - 上海中区广场 | 星期一, 2018-06-11 09:30 | ￥127500 / ￥136650 |
| 机器学习用于银行业务（使用Python） - 北京 - 创而新大厦 | 星期一, 2018-06-11 09:30 | ￥28810 / ￥33010 |