神经网络(Neural Networks,NN)培训课程 | Neural Networks培训课程

神经网络(Neural Networks,NN)培训课程

由讲师进行实时指导的神经网络本地培训课程通过互动讨论和动手实操演示了如何使用大量基本开源的工具包和软件库来构建神经网络,以及如何利用先进硬件(GPU)以及涉及分布式计算和大数据的优化技术的强大功能。我们的神经网络课程基于流行的编程语言,如Python、Java、R语言和具有强大功能的软件库,包括TensorFlow、Torch、Caffe、Theano等。我们的神经网络课程涵盖了理论和实现,使用了许多神经网络实现,如深度神经网络(DNN)、卷积神经网络(CNN)、递归神经网络(RNN)等。

神经网络培训形式包括“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。

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神经网络(Neural Networks,NN)子类别

神经网络(Neural Networks,NN)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
7小时
课程概览
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
7小时
课程概览
The training is aimed at people who want to learn the basics of neural networks and their applications.
14小时
课程概览
This course is an introduction to applying neural networks in real world problems using R-project software.
14小时
课程概览
This training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.
21小时
课程概览
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
35小时
课程概览
This course is created for people who have no previous experience in probability and statistics.
14小时
课程概览
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
28小时
课程概览
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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
14小时
课程概览
21小时
课程概览
This instructor-led, live 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.
21小时
课程概览
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
21小时
课程概览
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

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

- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
21小时
课程概览
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.

In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.

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

- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
7小时
课程概览
Snorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

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

- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.

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

- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
Encog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

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

- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
14小时
课程概览
在这一由讲师引导的现场培训中,参与者将学习如何使用Matlab来设计、构建、可视化用于图像识别的卷积神经网络。

在培训结束后,参与者将能够:

- 建立深度学习的模式
- 使数据分类自动化
- 使用Caffe和TensorFlow-Keras的模型
- 使用多个GPU、云或群集训练数据

受众

- 开发人员
- 工程师
- 领域专家

课程形式

- 部分讲座、部分讨论、练习和大量实操
35小时
课程概览
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
21小时
课程概览
深度强化学习是指“人工智能体”通过反复试验和奖惩来学习的能力。人工智能体旨在模仿人类直接从原始输入(如视觉)获取和构建知识的能力。为了实现强化学习,深度学习和神经网络会被用到。强化学习与机器学习不同,不依赖于有监督和无监督的学习方法。

在这一由讲师引导的现场培训中,学员将在逐步创建深度学习智能体的过程中学习深度强化学习的基础知识。

在本次培训结束后,学员将能够:

- 理解深度强化学习的基本概念,及其与机器学习的区别
- 运用先进的强化学习算法来解决实际问题
- 构建深度学习智能体

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
14小时
课程概览
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
28小时
课程概览
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
28小时
课程概览
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
21小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.

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

- Gain an overview of artificial intelligence, machine learning, and computational intelligence.
- Understand the concepts of neural networks and different learning methods.
- Choose artificial intelligence approaches effectively for real-life problems.
- Implement AI applications in mechatronic engineering.
14小时
课程概览
This instructor-led, live training in 中国 (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems.

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

- Create recommender systems at scale.
- Apply collaborative filtering to build recommender systems.
- Use Apache Spark to compute recommender systems on clusters.
- Build a framework to test recommendation algorithms with Python.
14小时
课程概览
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.

Format of the course

- Lecture and discussion coupled with hands-on exercises.

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