Deep Learning Training in Tashkent

This instructor-led, live training in Tashkent (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.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish 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 Tashkent (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

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

• Set up and navigate Google Colab for deep learning projects.
• Understand the fundamentals of neural networks.
• Implement deep learning models using TensorFlow.
• Train and evaluate deep learning models.
• Utilize advanced features of TensorFlow for deep learning.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish to specialize in cutting-edge deep learning techniques for NLU.

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

• Understand the key differences between NLU and NLP models.
• Apply advanced deep learning techniques to NLU tasks.
• Explore deep architectures such as transformers and attention mechanisms.
• Leverage future trends in NLU for building sophisticated AI systems.

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.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who aim to deepen their knowledge and enhance their skills in deep learning for text-to-image generation.

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

• Grasp advanced deep learning architectures and techniques tailored for text-to-image generation.
• Implement complex models and apply optimisations to achieve high-quality image synthesis.
• Enhance performance and scalability when working with large datasets and intricate models.
• Fine-tune hyperparameters to improve model performance and generalisation.
• Integrate Stable Diffusion with other deep learning frameworks and tools

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level professionals who wish to leverage AI techniques to revolutionize drug discovery and development processes.

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

• Understand the role of AI in drug discovery and development.
• Apply machine learning techniques to predict molecular properties and interactions.
• Use deep learning models for virtual screening and lead optimization.
• Integrate AI-driven approaches into the clinical trial process.

This instructor-led, live training in Tashkent (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.

This instructor-led, live training in Tashkent (online or onsite) is designed for beginner to intermediate developers interested in leveraging Large Language Models for various natural language tasks.

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

• Configure a development environment that incorporates a popular LLM.
• Construct a foundational LLM and fine-tune it using a custom dataset.
• Apply LLMs to diverse natural language tasks such as text summarization, question answering, text generation, and more.
• Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases.

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

• Understand the principles of Stable Diffusion and how it works for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this instructor-led, live training in Tashkent, participants will explore the most relevant and cutting-edge machine learning techniques in Python while building a series of demo applications involving image, music, text, and financial data.

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

• Implement machine learning algorithms and techniques to solve complex problems.
• Apply deep learning and semi-supervised learning approaches to applications involving image, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

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 instructor-led live training, delivered Tashkent (online or onsite), targets data scientists aiming to utilize TensorFlow for the analysis of potential fraud data.

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

• Create a fraud detection model in Python and TensorFlow.
• Build linear regressions and linear regression models to predict fraud.
• Develop an end-to-end AI application for analyzing fraud data.

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This instructor-led, live training in Tashkent (online or on-site) is designed for developers and data scientists who aim to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and more.

Upon completing this training, participants will be able to:

• Install and configure TensorFlow 2.x.
• Understand the advantages of TensorFlow 2.x compared to earlier versions.
• Develop deep learning models.
• Implement advanced image classifiers.
• Deploy deep learning models to cloud platforms, mobile devices, and IoT systems.

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