Deep Learning for Vision Training Course

This course explores the application of AI—specifically focusing on Machine Learning and Deep Learning—within the automotive industry. It guides learners in identifying which technologies can be effectively (or potentially) deployed across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making processes.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking a comprehensive overview of applied artificial intelligence and the near-term outlook for its development.

This instructor-led, live training in Brazil (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development.

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

• Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
• Write and understand procedural, functional, and object-oriented programming code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rules engines and expert systems for problem-solving.

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

Artificial Neural Networks are computational data models utilized in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one implementation of AI. Deep Learning constitutes a specialized subset of Machine Learning.

This four-day course provides an introduction to AI and its applications. Upon completion, there is an option to add an extra day to undertake a practical AI project.

Spanning four days, this course provides an introduction to Artificial Intelligence and its practical application using Python. Upon completing the course, participants have the option to add an extra day to work on a dedicated AI project.

This instructor-led, live training in Brazil (online or onsite) targets intermediate-level data scientists and statisticians who wish to prepare data, build models, and effectively apply machine learning techniques 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 utilized in the development of Artificial Intelligence (AI) systems designed to perform 'intelligent' tasks. These networks are frequently employed within Machine Learning (ML) applications, which represent one implementation of AI, while Deep Learning serves as a specialized subset of ML.

This instructor-led live training in Brazil (online or onsite) is designed for 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 begin creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Execute examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Brazil (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.

Upon completion of this training, participants will be able to:

• Apply fundamental statistical methods to pattern recognition.
• Utilize essential models, such as neural networks and kernel methods, for data analysis.
• Implement advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through their interaction with various environments. This technology drives numerous modern AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn optimal strategies, refine policies, and execute autonomous decisions via trial-and-error processes driven by reward signals.

This live training session, led by an instructor and available both online and in-person, is designed for intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The goal is to help participants build intelligent agents capable of making autonomous decisions within complex environments.

Upon completing this training, participants will be equipped to:

• Grasp the theoretical foundations and mathematical underpinnings of Reinforcement Learning.
• Implement core RL algorithms such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL techniques to real-world scenarios like gaming, robotics, and decision optimization.
• Debug, visualize, and enhance training performance using contemporary tools.

Format of the Course

• Interactive lectures combined with guided discussions.
• Practical, hands-on exercises and real-world implementations.
• Live coding demonstrations alongside project-based applications.

Course Customization Options

• For requests to tailor this course (for instance, utilizing PyTorch in place of TensorFlow), please contact us to coordinate the arrangement.

This instructor-led live training in Brazil (online or onsite) targets intermediate developers, data scientists, and AI practitioners who aim to utilize TensorFlow Lite for Edge AI applications.

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

• Understand 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.

This training is designed for individuals eager to grasp the fundamentals of neural networks and explore their practical applications.

This instructor-led, live training in Brazil (online or onsite) targets engineers who wish to write, load, and run machine learning models on very small embedded devices.

Upon completion of this training, participants will be able to:

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable capabilities such as speech detection and image classification.
• Integrate AI into hardware devices without depending on network connectivity.