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 Beirut or in NobleProg corporate training centers in Beirut.
NobleProg -- Your Local Training Provider
Mövenpick Hotel
Général de Gaulle Avenue, Beirut, Lebanon
The Mövenpick Hotel Beirut is one of the best business hotels with extensive meeting rooms. Meeting rooms are conveniently located on two interconnected floors with spacious pre-function areas and state-of-the-art technical facilities. A list of dedicated suppliers is hand-picked to provide organisers with a professional service across the board. Your events have never been so simple to organise.The property is only few hours drive from the cultural and historical sites. Set on 8,500 square meters of landscaped grounds, the hotel comprises conference facilities accommodating up to 1,200 invitees.The only city center business hotel with extensive resort facilities
Located in the heart of bustling Beirut, Ideally located on the Mediterranean Sea
In the heart of the capital 10 km from Beirut International Airport
In close proximity to the Beirut Central District and the upscale shopping area
This instructor-led, live training in Beirut (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.
By the end of this training, participants will be able to:
Build and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for scalable and efficient cloud-based model development.
Implement image preprocessing techniques for computer vision tasks.
Deploy computer vision models for real-world applications.
Use transfer learning to enhance the performance of CNN models.
Visualize and interpret the results of image classification models.
This instructor-led, live training in Beirut (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end of this training, participants will be able to:
Set up and navigate Google Colab for deep learning projects.
Understand the fundamentals of neural networks.
Implement deep learning models using TensorFlow.
Train and evaluate deep learning models.
Utilize advanced features of TensorFlow for deep learning.
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 Beirut, 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 Beirut (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 Beirut (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.
In this instructor-led, live training in Beirut (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 Beirut (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.
In this instructor-led, live training in Beirut, 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.
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 (4)
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
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
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