Introduction to the Use of Neural Networks Training Course

This program explores the application of AI—particularly Machine Learning and Deep Learning—within the automotive industry. It enables participants to identify which technologies can be (potentially) applied across various automotive scenarios, ranging from basic automation and image recognition to autonomous decision-making processes.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone seeking a comprehensive understanding of applied artificial intelligence and upcoming trends in its development.

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

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

• Grasp basic concepts in probability and statistics and apply them to real-world situations.
• 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.

Artificial Neural Networks serve as computational data models essential for developing Artificial Intelligence (AI) systems that can execute "intelligent" tasks. These networks are frequently utilized in Machine Learning (ML) applications, which represent one of the primary implementations of AI. Deep Learning constitutes a specialized subset within Machine Learning.

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

Designed as an intensive four-day program, this course provides a comprehensive introduction to Artificial Intelligence and its practical applications using Python. Upon completing the core curriculum, participants have the option to extend their learning by dedicating an additional day to a hands-on AI project.

This instructor-led, live training in Uzbekistan (online or onsite) is designed for intermediate-level data scientists and statisticians who wish to effectively prepare data, build models, and apply machine learning techniques within 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 effectively visualize results.
• 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 designed to execute "intelligent" tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one specific implementation of AI. Deep Learning constitutes a subset of Machine Learning.

This instructor-led, live training in Uzbekistan (online or on-site) is designed for researchers and developers who wish to use Chainer to build and train neural networks in Python while ensuring the code remains easy to debug.

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

• Set up the necessary development environment to begin creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Run practical examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for enhanced performance.

This instructor-led, live training in Uzbekistan (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.

Deep Reinforcement Learning (DRL) merges reinforcement learning concepts with deep learning architectures, empowering agents to make decisions through interaction with their surroundings. This technology drives numerous modern AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions via trial-and-error processes guided by reward mechanisms.

This instructor-led live training, available online or on-site, is designed for intermediate-level developers and data scientists eager to master and apply Deep Reinforcement Learning techniques. The goal is to equip participants with the skills needed to build intelligent agents capable of making autonomous decisions in 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 to practical scenarios like game development, robotics, and decision optimization.
• Troubleshoot, visualize, and enhance training performance using contemporary tools.

Course Format

• Interactive lectures and guided discussions.
• Practical exercises and real-world implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for instance, using PyTorch instead of TensorFlow), please contact us to arrange your specific needs.

This classroom-based training session will feature presentations and computer-based examples, along with case study exercises utilizing relevant neural and deep network libraries.

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

This course provides conceptual knowledge of neural networks, machine learning algorithms, and deep learning (algorithms and applications).

Part-1 (40%) of this training focuses on fundamentals but helps you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3 (40%) of the training would be extensively based on Tensorflow - API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN
• understand TensorFlow’s structure and deployment mechanisms
• be able to carry out installation / production environment / architecture tasks and configuration
• be able to assess code quality, perform debugging, monitoring
• be able to implement advanced production like training models, building graphs and logging

This instructor-led, live training in Uzbekistan (available online or on-site) is designed for advanced-level professionals seeking to explore state-of-the-art XAI techniques for deep learning models, with an emphasis on building interpretable AI systems.

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

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