TensorFlow Training Courses
Local, 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 "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in the UK or in NobleProg corporate training centers in the UK. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
Client Testimonials
Knowledgeable trainer
Sridhar Voorakkara
Course Name: 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 Name: Neural Networks Fundamentals using TensorFlow as Example
Very good all round overview.Good background into why Tensorflow operates as it does.
Kieran Conboy
Course Name: 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 Name: Neural Networks Fundamentals using TensorFlow as Example
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
Course Name: 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 Name: Neural Networks Fundamentals using TensorFlow as Example
Topic selection. Style of training. Practice orientation
Commerzbank AG
Course Name: Neural Networks Fundamentals using TensorFlow as Example
I really appreciated the crystal clear answers of Chris to our questions
Léo Dubus
Course Name: Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple
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Paper explanation
Course Name: Deep Learning with TensorFlow - bespoke
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Course Outlines
| Code | Name | Duration | Overview |
|---|---|---|---|
| tf101 | Deep Learning with TensorFlow | 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 |
| tfir | TensorFlow for Image Recognition | 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 |
| tsflw2v | Natural Language Processing with TensorFlow | 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 |
| dlv | Deep Learning for Vision | 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. |
| Neuralnettf | Neural Networks Fundamentals using TensorFlow as Example | 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 choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow. |
| deepmclrg | Machine Learning & Deep Learning with Python and R | 14 hours | |
| tpuprogramming | TPU Programming: Building Neural Network Applications on Tensor Processing Units | 7 hours | The Tensor Processing Unit (TPU) is the architecture which Google has used internally for several years, and is just now becoming available for use by the general public. It includes several optimizations specifically for use in neural networks, including streamlined matrix multiplication, and 8-bit integers instead of 16-bit in order to return appropriate levels of precision. In this instructor-led, live training, 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 Audience Developers Researchers Engineers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| embeddingprojector | Embedding Projector: Visualizing your Training Data | 14 hours | 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 |
| tensorflowserving | TensorFlow Serving | 7 hours | TensorFlow Serving is a system for serving machine learning (ML) models to production. In this instructor-led, live training, 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 Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| undnn | Understanding Deep Neural Networks | 35 hours | 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 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. |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 hours | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) 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 Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing) Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
Upcoming Courses
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| TensorFlow Serving - Newcastle | Fri, 2018-07-06 09:30 | £1300 / £1500 |
| Deep Learning with TensorFlow - York - Priory Street Centre | Mon, 2018-07-09 09:30 | £3900 / £4350 |
| Natural Language Processing with TensorFlow - Bradford - Carlisle Business Centre | Mon, 2018-07-09 09:30 | £6500 / £7750 |
| Understanding Deep Neural Networks - Portsmouth Technopole | Mon, 2018-07-09 09:30 | £6500 / £7250 |