Advanced Deep Learning Training Course

This course examines the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It guides learners in identifying which technologies can be effectively applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone interested in gaining an overview of applied artificial intelligence and its near-term development forecast.

Designed for beginners, this instructor-led, live training in Mexico (online or on-site) helps participants learn essential concepts in probability, statistics, programming, and machine learning, and apply them to AI development.

Upon completion, participants will be able to:

• Comprehend basic probability and statistics concepts and apply them to real-world situations.
• Write and interpret code using procedural, functional, and object-oriented paradigms.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rule engines and expert systems for effective problem-solving.

This instructor-led, live training in Mexico (online or in-person) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies.

By the end of this training, participants will be able to:

• Understand the basic principles of AlphaFold.
• Learn how AlphaFold works.
• Learn how to interpret AlphaFold predictions and results.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

Spanning four days, this course provides an introduction to artificial intelligence and its practical applications. Participants also have the option to extend their learning by dedicating an additional day to working on an AI project after completing the course.

This is a 4 day course introducing AI and its 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.

This instructor-led, live training in Mexico (online or on-site) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

By the end of this training, participants will be able to:

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post-hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

An Artificial Neural Network is a computational data model employed in creating Artificial Intelligence (AI) systems that can carry out "intelligent" tasks. These networks are frequently utilized in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a subset of ML.

This instructor-led live training in Mexico (online or onsite) targets researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring the code is 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 comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.

This instructor-led, live training in Mexico (online or onsite) 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.

By the end of this training, participants will be able to:

• Apply core statistical methods to pattern recognition.
• Use key models like neural networks and kernel methods for data analysis.
• Implement advanced techniques for complex problem-solving.
• Improve prediction accuracy by combining different models.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions via interaction with their surroundings. This technology drives numerous contemporary AI breakthroughs, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. Through reward-based learning, a DRL agent can learn strategies, optimize policies, and execute autonomous decisions through trial and error.

This instructor-led live training (available online or on-site) targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques to develop intelligent agents capable of making autonomous decisions in complex environments.

Upon completing this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical scenarios like gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Interactive lectures and guided discussions.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Customization Options

• To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

This instructor-led, live training in Mexico (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners seeking to leverage TensorFlow Lite for Edge AI applications.

By the conclusion of this training, participants will be able to:

• Comprehend the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

Type: Theoretical training with predetermined practical exercises in Lasagne or Keras, depending on the group’s pedagogical structure.

Pedagogical method: Presentations, discussions, and case studies.

Artificial Intelligence has recently disrupted many scientific fields and is now revolutionizing a wide range of economic sectors (industry, medicine, communication, etc.). However, media portrayals of AI often rely on fantasy and are far removed from the reality of Machine Learning or Deep Learning. This course aims to provide engineers—already proficient in computing tools and with a foundation in software programming—with an introduction to Deep Learning, its various specialization areas, and the main network architectures available today. While the mathematical fundamentals will be reviewed during the course, a college-level (BAC+2) math background is recommended for greater comfort. Although it is possible to skip the mathematical aspects and adopt a purely "system" view, this approach would significantly limit your understanding of the subject.

This instructor-led, live training in Mexico (online or onsite) is designed for engineers who aim to write, load, and run machine learning models on highly compact embedded devices.

By the end of this training, participants will be able to:

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.