深度学习辅导课程

本地的，具有指导作用的现场深度学习（DL）培训课程通过实践深入学习的基础知识和应用程序进行演示，并涵盖深入机器学习，深层次学习和分层学习等主题。深度学习培训可作为“现场实时培训”或“远程实时培训”。现场实地培训可在当地客户现场进行中国或者在考而思教育公司的培训中心中国 。远程实时培训通过交互式远程桌面进行。

DeepLearning(DL)课程大纲

Python用于高级机器学习

21小时 在这一由讲师引导的现场培训中，参与者将学习Python中最相关及最尖端的机器学习技术，因为它们构建了一系列涉及图像、音乐、文本和财务数据的演示应用程序。在本次培训结束后，参与者将能够：- 运用用于解决复杂问题的机器学习算法和技术- 将深度学习和半监督学习应用于涉及图像、音乐、文本和财务数据的应用程序- 推动Python算法达到其最大潜力- 使用例如NumPy和Theano的库和包受众- 开发人员- 分析师- 数据科学家课程形式- 部分讲座、部分讨论、练习和大量实操

用Python进行深度强化学习

21小时 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

用于电信行业的深度学习（使用Python）

28小时 机器学习是人工智能的一个分支，指计算机可以在不被明确编程的情况下学习。深度学习是机器学习的一个子领域，它使用基于学习数据表示和结构（例如神经网络）的方法。Python是一种高级编程语言，以其清晰的语法和代码易读性而闻名。在这一由讲师引导的现场培训中，学员将逐步学习如何创建深度学习信用风险模型，从而学习如何使用Python实现用于电信行业的深度学习模型。在本次培训结束后，学员将能够：- 了解深度学习的基本概念。- 了解深度学习在电信行业中的应用和用途。- 使用Python、Keras、TensorFlow创建用于电信行业的深度学习模型。- 使用Python构建自己的深度学习客户流失预测模型。课程形式- 互动讲座和讨论。- 大量练习和实操。- 在现场实验室环境中动手实现。课程自定义选项- 如需本课程的定制培训，请联系我们以作安排。

Artificial Neural Networks, Machine Learning, Deep Thinking

21小时 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.

Introduction to Deep Learning

21小时 This course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

Advanced Deep Learning

28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

Deep Learning for Vision with Caffe

21小时 Caffe is a deep learning framework made with expression, speed, and modularity in mind.This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an exampleAudienceThis course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.After completing this course, delegates will be able to:- understand Caffe’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, implementing layers and logging

Deep Learning for Vision

21小时 AudienceThis course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer imagesThis course provide working examples.

Artificial Intelligence in Automotive

14小时 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.

Machine Learning and Deep Learning

21小时 This course covers AI (emphasizing Machine Learning and Deep Learning)

深度学习基础与实战

14小时

OpenNN: Implementing Neural Networks

14小时 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.

OpenNMT: Setting Up a Neural Machine Translation System

7小时 In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.Source and target language samples will be pre-arranged per the audience's requirements.Format of the Course- Part lecture, part discussion, heavy hands-on practice

Introduction Deep Learning and Neural Network for Engineers

21小时 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.

Facebook NMT: Setting up a Neural Machine Translation System

7小时 In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content.By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.Format of the course- Part lecture, part discussion, heavy hands-on practiceNote- If you wish to use specific source and target language content, please contact us to arrange.

Microsoft Cognitive Toolkit 2.x

21小时 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 algorithmsAudience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practiceNote- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.

PaddlePaddle

21小时 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 scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

Amazon DSSTNE: Build a Recommendation System

7小时 In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.By the end of this training, participants will be able to:- Train a recommendation model with sparse datasets as input- Scale training and prediction models over multiple GPUs- Spread out computation and storage in a model-parallel fashion- Generate Amazon-like personalized product recommendations- Deploy a production-ready application that can scale at heavy workloadsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

T2T: Creating Sequence to Sequence Models for Generalized Learning

7小时 Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.By the end of this training, participants will be able to:- Install tensor2tensor, select a data set, and train and evaluate an AI model- Customize a development environment using the tools and components included in Tensor2Tensor- Create and use a single model to concurrently learn a number of tasks from multiple domains- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited- Obtain satisfactory processing results using a single GPUAudience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

OpenFace: Creating Facial Recognition Systems

14小时 OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.By the end of this training, participants will be able to:- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.Audience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

Advanced Machine Learning with R

21小时 In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.By the end of this training, participants will be able to:- Understand and implement unsupervised learning techniques- Apply clustering and classification to make predictions based on real world data.- Visualize data to quicly gain insights, make decisions and further refine analysis.- Improve the performance of a machine learning model using hyper-parameter tuning.- Put a model into production for use in a larger application.- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

Matlab：用于深度学习

14小时 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

Deep Learning for Finance (with R)

28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.By the end of this training, participants will be able to:- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in finance- Use R to create deep learning models for finance- Build their own deep learning stock price prediction model using RAudience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

Deep Learning for Banking (with R)

28小时 Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.By the end of this training, participants will be able to:- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in banking- Use R to create deep learning models for banking- Build their own deep learning credit risk model using RAudience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

Deep Learning AI Techniques for Executives, Developers and Managers

21小时 Introduction:Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.Target Audience:( Depending on target audience, course materials will be customized)ExecutivesA general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.Project ManagersHow to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.DevelopersIn-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.SalespersonsA general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.

Neural computing – Data science

14小时 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

Deep Learning for Medicine

14小时 Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.By the end of this training, participants will be able to:- Understand the fundamentals of Deep Learning- Learn Deep Learning techniques and their applications in the industry- Examine issues in medicine which can be solved by Deep Learning technologies- Explore Deep Learning case studies in medicine- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicineAudience- Managers- Medical professionals in leadership rolesFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practiceNote- To request a customized training for this course, please contact us to arrange.

Hardware-Accelerated Video Analytics

14小时 This instructor-led, live training in 中国 (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.By the end of this training, participants will be able to:- Install and configure the necessary development environment, software and libraries to begin developing.- Build, train, and deploy deep learning models to analyze live video feeds.- Identify, track, segment and predict different objects within video frames.- Optimize object detection and tracking models.- Deploy an intelligent video analytics (IVA) application.

Deep Learning for Healthcare

14小时 This instructor-led, live training in 中国 (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.By the end of this training, participants will be able to:- Install and configure the necessary development environment, software and libraries to begin developing.- Analyze MRI images using deep learning techniques such as CNNs.- Detect potential health conditions such as heart disease through MRI scan analysis.- Apply techniques such as image segmentation and CNN training to identify potential disease.- Identify the genomics of a disease using radiomics.- Build and deploy a deep learning application aimed at healthcare image analysis.

Deep Learning for Business

14小时 This instructor-led, live training in 中国 (online or onsite) is aimed at business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.By the end of this training, participants will be able to:- Understand the core concepts of machine learning and deep learning.- Get insights on the future of business and industry with ML and DL.- Define business strategies and solutions with deep learning.- Learn how to apply data science and deep learning in solving business problems.- Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.