Advanced Machine Learning with Python Training Course

This course explores the application of AI—specifically Machine Learning and Deep Learning—within the automotive industry. It aims to identify technologies suitable for various in-car scenarios, 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 introduction to applied artificial intelligence and its upcoming development trends.

This instructor-led, live training in South Korea (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 the development of Artificial Intelligence (AI) systems designed to perform 'intelligent' tasks. Neural Networks are widely employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning serves as a specialized subset of Machine Learning.

This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.

This instructor-led live training in South Korea (online or onsite) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python, while 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 begin building neural network models.
• Define and implement neural network models using clear and comprehensible source code.
• Execute examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high-performance computing.

This instructor-led, live training in South Korea (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of computer vision and explore TensorFlow’s capabilities for developing sophisticated vision models using Google Colab.

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

• Build and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led, live training in South Korea (online or onsite) offers a foundational introduction to the fields of pattern recognition and machine learning. It explores 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 fundamental statistical methods to pattern recognition.
• Leverage key models such as neural networks and kernel methods for effective data analysis.
• Deploy advanced techniques to solve complex problems.
• Enhance prediction accuracy by integrating various models.

Deep Reinforcement Learning (DRL) integrates the principles of reinforcement learning with deep learning structures, empowering agents to make decisions through environmental interaction. This technology drives numerous modern AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to refine policies, learn strategies, and execute autonomous decisions by leveraging trial-and-error mechanisms and reward-based feedback.

This instructor-led training session, available both online and onsite, is designed for intermediate developers and data scientists looking to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills necessary to develop intelligent agents capable of making autonomous decisions in complex scenarios.

Upon completing this course, participants will be able to:

• Grasp the theoretical foundations and mathematical core of Reinforcement Learning.
• Code essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions for practical use cases, including gaming, robotics, and optimization tasks.
• Utilize contemporary tools to visualize, troubleshoot, and optimize training performance.

Course Format

• Engaging lectures paired with guided discussions.
• Practical exercises and real-world implementation scenarios.
• Live coding sessions and project-based applications.

Customization Options

• For customized course versions (e.g., switching from TensorFlow to PyTorch), please reach out to us to make arrangements.

This instructor-led live training, offered in South Korea (online or onsite), targets intermediate developers, data scientists, and AI practitioners eager to harness TensorFlow Lite for Edge AI solutions.

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

• Comprehend 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 instructor-led, live training at South Korea (online or on-site) targets data scientists who want to accelerate real-time machine learning applications and deploy them at scale.

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

• Install the OpenVINO toolkit.
• Accelerate a computer vision application using an FPGA.
• Run different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in South Korea (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel.

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

• Set up the necessary development environment to start running deep learning trainings.
• Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
• Scale deep learning training with Horovod to run on multiple GPUs.

This course provides foundational knowledge of neural networks and their broader role within machine learning and deep learning algorithms and applications.

The first segment (40%) of the training emphasizes core fundamentals, equipping you to select appropriate technologies such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

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

The third segment (40%) focuses extensively on TensorFlow, the open-source software library for deep learning developed by Google. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

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

Upon completion, participants will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• comprehend the structure and deployment mechanisms of TensorFlow
• possess the skills to manage installation, production environment setup, architecture tasks, and configuration
• be capable of assessing code quality, performing debugging, and monitoring systems
• be able to implement advanced production-level tasks, including model training, graph construction, and logging

This instructor-led, live training in South Korea (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.