Neural Networks Training in Lyon

This instructor-led live training in Lyon (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building 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 course empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions entirely from scratch using Python. It explores the core principles of supervised learning (including classification and regression) and unsupervised learning (such as clustering and anomaly detection), alongside advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The curriculum supports professionals in implementing practical ML models, assessing algorithmic limitations, and completing applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning models, empowering agents to make decisions by interacting with their surroundings. This technology drives many modern AI innovations, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, refine policies, and make autonomous choices through trial and error, guided by reward-based feedback.

This instructor-led training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. Participants will learn to build intelligent agents capable of making autonomous decisions in complex environments.

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

• Grasp the theoretical foundations and mathematical principles underlying Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to practical scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Format of the Course also allows for the evaluation of participants.

• Interactive lectures accompanied by guided discussions.
• Hands-on exercises and practical implementation tasks.
• 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 make arrangements.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automating processes, and enhancing decision-making across enterprise operations. This course explores core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside communication, perception, and autonomous action. It equips executives and architects with the insights needed to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to accelerate business agility.

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive industry. It aids in identifying technologies suitable for various in-vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

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

This instructor-led, live training in Lyon (online or on-site) 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.

This course provides foundational knowledge on neural networks, machine learning algorithms, and the principles and applications of deep learning.

Part 1 (40%) of the training emphasizes fundamentals, enabling you to select the appropriate technology stack, such as TensorFlow, Caffe, Theano, DeepDrive, or Keras.

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

Part 3 (40%) focuses extensively on TensorFlow, Google's open-source software library API 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 of this course, participants will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend TensorFlow’s architecture and deployment mechanisms
• be capable of managing installation, production environments, and architectural configurations
• be able to evaluate code quality, perform debugging, and monitor performance
• be proficient in implementing advanced production tasks such as training models, constructing graphs, and logging