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 trainings in the US can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
I really appreciated the crystal clear answers of Chris to our questions.
Léo Dubus
Course: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
I generally enjoyed the knowledgeable trainer.
Sridhar Voorakkara
Course: Neural Networks Fundamentals using TensorFlow as Example
I was amazed at the standard of this class - I would say that it was university standard.
David Relihan
Course: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview. Good background into why Tensorflow operates as it does.
Kieran Conboy
Course: Neural Networks Fundamentals using TensorFlow as Example
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
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course: TensorFlow for Image Recognition
Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
I was benefit from topic selection. Style of training. Practice orientation.
Commerzbank AG
Course: Neural Networks Fundamentals using TensorFlow as Example
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
the way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The excersise where we should train a network to approximate a function
Nercia Utbildning AB
Course: Deep Learning with TensorFlow 2.0
The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.
Robert Baker
Course: Deep Learning with TensorFlow 2.0
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
A wide range of topics covered and substantial knowledge of the leaders.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Lack
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Big theoretical and practical knowledge of the lecturers. Communicativeness of trainers. During the course, you could ask questions and get satisfactory answers.
Kamil Kurek - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Practical part, where we implemented algorithms. This allowed for a better understanding of the topic.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
exercises and examples implemented on them
Paweł Orzechowski - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Examples and issues discussed.
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Substantive knowledge, commitment, a passionate way of transferring knowledge. Practical examples after a theoretical lecture.
Janusz Chrobot - ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Practical exercises prepared by Mr. Maciej
ING Bank Śląski S.A.; Kamil Kurek Programowanie
Course: Understanding Deep Neural Networks
Machine Translated
Human identification and circuit board bad point detection
王 春柱 - 中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Demonstrate
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
A lot of practical tips
Pawel Dawidowski - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
A lot of information related to the implementation of solutions
Michał Smolana - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
A multitude of practical tips and knowledge of the lecturer from a wide range of AI / IT / SQL / IoT issues.
ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Course: Deep Learning with TensorFlow 2.0
TensorFlow Course Outlines
Course Name
Duration
Overview
Course Name
Duration
Overview
14 hours
Embedding Projector is an opensource web application for visualizing the data used to train machine learning systems Created by Google, it is part of TensorFlow
This instructorled, 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 realworld 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 handson practice
.
21 hours
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
28 hours
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
35 hours
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
21 hours
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.
28 hours
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.
7 hours
In this instructor-led, live training in the US, 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.
7 hours
In this instructor-led, live training in the US (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.
35 hours
This course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications)
Part1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc
Part2(20%) of this training introduces Theano a python library that makes writing deep learning models easy
Part3(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
.
28 hours
In this instructor-led, live training in the US, 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.
28 hours
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.
21 hours
This instructor-led, live training in the US (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.
14 hours
This instructor-led, live training in the US (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.
14 hours
This instructor-led, live training in the US (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.
21 hours
This instructor-led, live training in the US (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.
28 hours
This instructor-led, live training in the US (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.
Last Updated:
Upcoming TensorFlow Courses
TensorFlow for Image Recognition
06/19/2023 - 09:30
NY, Queens - Forest Hills Tower
28 hours
Natural Language Processing (NLP) with TensorFlow
07/03/2023 - 09:30
NY, Queens - Forest Hills Tower
35 hours
Neural Networks Fundamentals using TensorFlow as Example
07/17/2023 - 09:30
NY, Queens - Forest Hills Tower
28 hours
Deep Learning with TensorFlow
07/31/2023 - 09:30
NY, Queens - Forest Hills Tower
21 hours
Neural Networks Fundamentals using TensorFlow as Example
08/14/2023 - 09:30
NY, Queens - Forest Hills Tower
28 hours
Natural Language Processing (NLP) with TensorFlow
08/28/2023 - 09:30
NY, Queens - Forest Hills Tower
35 hours
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