Neural Networks Training in Maia

This instructor-led, live training in Maia (online or onsite) is targeted at advanced-level professionals who wish to investigate cutting-edge XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

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

• Understand the challenges of explainability in deep learning.
• Implement advanced XAI techniques for neural networks.
• Interpret decisions made by deep learning models.
• Evaluate the trade-offs between performance and transparency.

This four-day course provides an introduction to Artificial Intelligence and its practical application using the Python programming language. Participants have the option to extend their learning by adding an extra day dedicated to completing a real-world AI project upon finishing the course.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through environmental interaction. This technology drives numerous contemporary AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. By utilizing reward-based learning, DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions via trial and error.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers and data scientists seeking to master and apply Deep Reinforcement Learning techniques to develop intelligent agents capable of autonomous decision-making within complex environments.

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

• Grasp the theoretical foundations and mathematical principles underpinning 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 applications, including games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance with modern tools.

Format of the Course

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

Course Customization Options

• To request a customized version of this course (for example, utilizing PyTorch instead of TensorFlow), please contact us to arrange.

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

This course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive industry. It aids in identifying which technologies can be (potentially) applied across various scenarios in a vehicle, ranging from basic automation and image recognition to autonomous decision-making.

This instructor-led, live training in Maia (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:

• Comprehend core principles of probability and statistics, and apply them to real-world situations.
• Write and interpret code using procedural, functional, and object-oriented approaches.
• Implement machine learning techniques including classification, clustering, and neural networks.
• Develop AI solutions using rule engines and expert systems for problem-solving.

Artificial Neural Networks are computational data models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as one of the primary implementations of AI. Deep Learning constitutes a specialized subset of Machine Learning.

This four-day course provides an introduction to Artificial Intelligence and its practical applications. An additional day is available to undertake an AI project upon completion of the course.

This instructor-led, live training in Maia (online or onsite) is designed for data scientists and statisticians at an intermediate level who wish to effectively prepare data, build models, and 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.

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

This instructor-led, live training in Maia (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.

This instructor-led, live training in Maia (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.

In this instructor-led live training, participants will learn how to utilize 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

This training is designed for individuals seeking to grasp the fundamental principles of neural networks and their practical applications.

This instructor-led training session combines presentations with practical, computer-based examples and case study exercises, utilizing relevant neural and deep learning libraries.

This course provides an introduction to applying neural networks to real-world problems using the R-project software.

This course provides a foundational understanding of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

Part 2 (20%) of the training introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) of the training is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers looking to utilize TensorFlow for their deep learning projects.

Upon completion, participants will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• Comprehend TensorFlow’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Assess code quality, debug, and monitor systems
• Implement advanced production-like tasks such as training models, building graphs, and logging