Neural Networks Training in Tashkent

This instructor-led, live training in Tashkent (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.

Applied AI from Scratch in Python empowers programmers and data analysts with the essential techniques needed to construct machine learning solutions from the ground up using Python. The course covers the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning methods such as clustering and anomaly detection, alongside advanced neural network architectures. Participants will explore effective methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. This training enables professionals to implement real-world machine learning models, assess algorithmic limitations, and complete applied projects aimed at solving practical problems.

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.

Delving into the foundations of artificial intelligence reveals how smart technologies are transforming enterprise operations through digital strategy, automation, and enhanced decision-making. The course covers essential concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning approaches. It also explores capabilities related to communication, perception, and autonomous behavior. Designed for executives and architects, this program provides guidance on assessing AI-driven transformation opportunities, understanding emerging tech trends, and implementing practical intelligent solutions to boost business agility.

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.

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 instructor-led, live training in Tashkent (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.

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