TensorFlow辅导课程

由讲师进行实时指导的TensorFlow 培训课程导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考而思教育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。

TensorFlow课程大纲

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.AudienceThis course is intended for engineers seeking to use TensorFlow for their Deep Learning projectsAfter 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

TensorFlow for Image Recognition

28小时 This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognitionAudienceThis course is intended for engineers seeking to utilize TensorFlow for the purposes of Image RecognitionAfter 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

Natural Language Processing with TensorFlow

35小时 TensorFlow? is an open source software library for numerical computation using data flow graphs.SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.AudienceThis course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.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, embedding terms, building graphs 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.

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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.

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 loversAudience- Developers- Data scientistsFormat of the course- Part lecture, part discussion, exercises and heavy hands-on practice

TensorFlow Serving

7小时 In this instructor-led, live training in 中国 (online or onsite), 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.

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.AudienceThis course is intended for engineers seeking to use TensorFlow for their Deep Learning projectsAfter 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

Deep Learning for NLP (Natural Language Processing)

28小时 In this instructor-led, live training in 中国, participants will learn to use Python libraries for NLP 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.

Applied AI from Scratch

28小时 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.

Deep Learning with TensorFlow 2

21小时 This instructor-led, live training in 中国 (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.By the end of this training, participants will be able to:- Install and configure TensorFlow 2.x.- Understand the benefits of TensorFlow 2.x over previous versions.- Build deep learning models.- Implement an advanced image classifier.- Deploy a deep learning model to the cloud, mobile and IoT devices.

Machine Learning with TensorFlow.js

14小时 This instructor-led, live training in 中国 (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 existing machine learning models in the browser or under Node.js.- Retrain pre-existing machine learning using custom data.

TensorFlow Extended (TFX)

21小时 This instructor-led, live training in 中国 (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.

Fraud Detection with Python and TensorFlow

14小时 This instructor-led, live training in 中国 (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.