AI-Driven Drug Discovery and Development Training Course

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.

This program has been designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone interested in gaining an understanding of applied artificial intelligence and its near-term development trends.

This instructor-led, live training in France (online or onsite) is designed for beginners who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply them to AI development.

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

• Grasp fundamental concepts in probability and statistics and apply them to real-world scenarios.
• Write and comprehend 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 France (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.

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 four-day course provides an introduction to Artificial Intelligence and its practical applications. Participants also have the option to extend the training by one additional day to work on a hands-on AI project upon completion of the course.

This is a 4-day course introducing AI and its applications using the Python programming language. There is an option to have an additional day to undertake an AI project upon completion of this course.

This instructor-led, live training in France (online or onsite) is designed for intermediate-level data scientists and statisticians who aim to effectively prepare data, build models, and apply machine learning techniques within their respective fields.

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

• Comprehend and implement a variety of Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Perform 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 that can perform 'intelligent' tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a subset of Machine Learning.

This instructor-led live training in France (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 France (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.

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.

This instructor-led, live training in France (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

By the end 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.

Type: Theoretical training with predefined practical applications chosen in advance with the students using either Lasagne or Keras, depending on the pedagogical group.

Pedagogical method: Presentation, discussion, and case studies.

Artificial Intelligence, after revolutionizing numerous scientific fields, has begun to transform a wide range of economic sectors (industry, medicine, communication, etc.). However, its portrayal in major media often leans toward fantasy, far removed from the reality of what Machine Learning and Deep Learning actually entail. The objective of this training is to provide engineers who already have a solid grasp of computing tools (including a foundation in software programming) with an introduction to Deep Learning, its various specialized domains, and the main network architectures currently in existence. While mathematical foundations will be reviewed during the course, a level of mathematics equivalent to BAC+2 (two years of higher education) is recommended for greater comfort. It is technically possible to skip the mathematical focus and adopt a purely 'system' perspective, but this approach will significantly limit your understanding of the subject.

This instructor-led, live training in France (online or onsite) is tailored for engineers who want to write, load, and run machine learning models on very small 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.