Online or onsite, instructor-led live TensorFlow training courses demonstrate through interactive discussion and hands-on practice how to use the TensorFlow system to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.
TensorFlow training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live TensorFlow training can be carried out locally on customer premises in SanYa or in NobleProg corporate training centers in SanYa.
NobleProg -- Your Local Training Provider
Hainan - Four Points by Sheraton
No.78, Sanya Bay Road, Sanya, Hainan, China, Sanya, China, 572000
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The 5-star Fourpoints by Sheraton Hainan Sanya offers relaxing beach-side living with an outdoor pool and a wellness centre. A 1-minute walk from the beach, the luxurious hotel features karaoke facilities and upscale rooms with a laptop safe box. On-site parking and Wi-Fi in public areas are free. Fourpoints by Sheraton is a 35-minute drive from Duty Free Shop or Sanya Railway Station and a 15-minute drive from Phoenix International Airport. It is a 10-minute drive from East Island and West Island. Tianya Haijiao is a 20-minute drive away, while the Guanyin Statue of Hainan is a 40-minute car journey away. All rooms are fitted with a flat-screen TV, a minibar and an iron. A writing desk and a seating area are also included. En suite bathrooms have both a bathtub and shower facilities. Guests can work out at the gym, play tennis on site, or check emails at the business centre. The tour desk can help guests organise cycling trips. There is a gift shop on site for last minute purchase. Japanese, Chinese and Western cuisine can be enjoyed at the hotel’s restaurants. We speak your language! Four Points by Sheraton Hainan, Sanya has been welcoming Booking.com guests since 7 Dec 2012 Hotel Rooms: 370, Hotel Chain: Four Points by Sheraton
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
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.
In this instructor-led, live training in SanYa, 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.
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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.
By the end of this training, participants will be able to:
Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
Use OpenShift to simplify the work of initializing a Kubernetes cluster.
Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.
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.
This instructor-led, live training in SanYa (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.
This instructor-led, live training in SanYa (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.
In this instructor-led, live training in SanYa (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.
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
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
This instructor-led, live training in SanYa (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.
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.
Audience
This 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
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
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Testimonials (3)
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course - Neural Networks Fundamentals using TensorFlow as Example
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