Mexico City - Colonia Del Valle

Av. Insurgentes Sur N° 800 , Mexico City, mexico, 3100
The Colonia Del Valle (HQ) Center is on the eighth floor of an attractive, 20-story steel-gray building on the Avenida de los...

Mexico City - Torre Angel

Paseo de la Reforma 350, Mexico City, mexico, 6600
The Mexico City Torre Angel Center is on the 10th and 11th floors of a prestigious curved, reflective high-rise building with...

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OpenNN

Explore Our Courses

Applied AI from Scratch in Python

 28 Hours
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 
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Deep Reinforcement Learning with Python

 21 Hours
This instructor-led, live training in Mexico City (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to:
  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems.
  • Build a Deep Learning Agent.
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Artificial Intelligence (AI) Overview

 7 Hours
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
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Artificial Intelligence (AI) in Automotive

 14 Hours
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
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From Zero to AI

 35 Hours
This course is created for people who have no previous experience in probability and statistics.
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Artificial Neural Networks, Machine Learning, Deep Thinking

 21 Hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
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Applied AI from Scratch

 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. 
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Applied Machine Learning

 14 Hours
This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
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Artificial Neural Networks, Machine Learning and Deep Thinking

 21 Hours
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
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Deep Learning Neural Networks with Chainer

 14 Hours
This instructor-led, live training in Mexico City (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start developing neural network models.
  • Define and implement neural network models using a comprehensible source code.
  • Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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Pattern Recognition

 21 Hours
This instructor-led, live course provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.
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Introduction Deep Learning & Réseaux de neurones pour l’ingénieur

 21 Hours
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
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Matlab for Deep Learning

 14 Hours
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition. By the end of this training, participants will be able to:
  • Build a deep learning model
  • Automate data labeling
  • Work with models from Caffe and TensorFlow-Keras
  • Train data using multiple GPUs, the cloud, or clusters
Audience
  • Developers
  • Engineers
  • Domain experts
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Artificial Intelligence (AI) for Mechatronics

 21 Hours
This instructor-led, live training in Mexico City (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems. By the end of this training, participants will be able to:
  • Gain an overview of artificial intelligence, machine learning, and computational intelligence.
  • Understand the concepts of neural networks and different learning methods.
  • Choose artificial intelligence approaches effectively for real-life problems.
  • Implement AI applications in mechatronic engineering.
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Introduction to the Use of Neural Networks

 7 Hours
The training is aimed at people who want to learn the basics of neural networks and their applications.
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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 to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
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Neural computing – Data science

 14 Hours
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
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PaddlePaddle

 21 Hours
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu. In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications. By the end of this training, participants will be able to:
  • Set up and configure PaddlePaddle
  • Set up a Convolutional Neural Network (CNN) for image recognition and object detection
  • Set up a Recurrent Neural Network (RNN) for sentiment analysis
  • Set up deep learning on recommendation systems to help users find answers
  • Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Recommender Systems with Python

 14 Hours
This instructor-led, live training in Mexico City (online or onsite) is aimed at data scientists who wish to use Python to build recommender systems. By the end of this training, participants will be able to:
  • Create recommender systems at scale.
  • Apply collaborative filtering to build recommender systems.
  • Use Apache Spark to compute recommender systems on clusters.
  • Build a framework to test recommendation algorithms with Python.
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Neural Network in R

 14 Hours
This course is an introduction to applying neural networks in real world problems using R-project software.
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TPU Programming: Building Neural Network Applications on Tensor Processing Units

 7 Hours
In this instructor-led, live training in Mexico City, 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.
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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
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OpenNN: Implementing Neural Networks

 14 Hours
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
  • Lecture and discussion coupled with hands-on exercises.
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Testimonials (5)

Working from first principles in a focused way, and moving to applying case studies within the same day

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking

It felt like we were going through directly relevant information at a good pace (i.e. no filler material)

Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to the use of neural networks

That it was applying real company data. Trainer had a very good approach by making trainees participate and compete

Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python

Very flexible.

Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking

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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