Introduction to Deep Learning Training Course

This course provides a comprehensive overview of AI, with a focus on Machine Learning and Deep Learning, applied within the automotive industry. It helps participants identify which technologies can be (potentially) utilized across various scenarios in vehicles: 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 seeking a comprehensive overview of applied artificial intelligence and its near-term development forecasts.

This instructor-led, live training in Turkey (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 Turkey (online or onsite) is designed for biologists who wish to understand the workings of AlphaFold and apply its models to direct 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 that can execute "intelligent" tasks. Neural Networks are frequently applied within Machine Learning (ML) environments, which serve as one of the primary implementations of AI. Deep Learning represents a specialized subset of Machine Learning.

This four-day course provides an introduction to AI and its practical applications. Upon completion, participants have the option to extend their learning with an additional day dedicated to working on an AI project.

This four-day course provides an introduction to artificial intelligence and its practical application using the Python programming language. Participants may opt to add an extra day to complete an AI project upon finishing the course.

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

Artificial Neural Networks are computational data models employed in the development of Artificial Intelligence (AI) systems that can execute "intelligent" tasks. These networks are widely utilized in 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 Turkey (online or onsite) is targeted at researchers and developers who wish to utilize 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 Turkey (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 the principles of reinforcement learning with deep learning architectures, empowering agents to make intelligent decisions through interaction with their surroundings. This technology is foundational to numerous modern AI breakthroughs, including autonomous vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. By leveraging reward-based learning, DRL enables artificial agents to refine strategies, optimize policies, and execute autonomous decisions via a trial-and-error process.

This instructor-led live training, available online or onsite, targets intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills to build intelligent agents capable of autonomous decision-making within complex environments.

Upon completing 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 techniques to practical domains like gaming, robotics, and decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures paired with guided discussions.
• Practical exercises and hands-on implementation projects.
• Live coding demonstrations and application-focused projects.

Customization Options

• For customized course variations (such as switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals of TensorFlow Lite and its function in Edge AI.
• Create and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models across various edge devices.
• Apply tools and techniques for model conversion and optimization.
• Develop practical Edge AI applications using TensorFlow Lite.

This training is designed for individuals seeking to understand the fundamentals of neural networks and their practical applications.

This instructor-led, live training in Turkey (online or onsite) targets engineers aiming to develop, deploy, and operate machine learning models on highly resource-limited embedded devices.

Upon completing this training, participants will be able to:

• Install TensorFlow Lite.
• Deploy machine learning models onto embedded devices to facilitate tasks such as speech detection and image classification.
• Integrate AI capabilities into hardware devices without dependence on network connectivity.