Deep Learning Training in Ankara

This instructor-led, live training in Ankara (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 instructor-led, live training in Ankara (available online or onsite) is designed for advanced professionals aiming to expand their expertise in computer vision and investigate TensorFlow's potential for creating sophisticated vision models via Google Colab.

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

• Construct 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 Ankara (online or onsite) targets 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:

• Configure and navigate Google Colab for deep learning projects.
• Grasp the core principles of neural networks.
• Develop deep learning models utilizing TensorFlow.
• Train and assess deep learning models.
• Leverage advanced TensorFlow features for deep learning applications.

This instructor-led, live training in Ankara (available online or on-site) is designed for advanced professionals seeking 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.

Designed for advanced professionals seeking to apply state-of-the-art XAI techniques to deep learning models and build interpretable AI systems, this instructor-led live training is available both online and onsite.

Upon completing this training, participants will be able to:

• Grasp the challenges associated with explainability in deep learning.
• Apply advanced XAI techniques to neural networks.
• Interpret the decisions generated by deep learning models.
• Assess the trade-offs between model performance and transparency.

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

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

• Understand advanced deep learning architectures and techniques for text-to-image generation.
• Implement complex models and optimizations for high-quality image synthesis.
• Optimize performance and scalability for large datasets and complex models.
• Tune hyperparameters for better model performance and generalization.
• Integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led live training in Ankara (online or onsite) is designed for advanced professionals looking to use AI techniques to transform 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 Ankara (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 Ankara (online or onsite) is designed for beginner to intermediate data scientists and machine learning engineers looking to enhance their deep learning model performance.

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

• Understand the principles of distributed deep learning.
• Install and configure DeepSpeed.
• Scale deep learning models on distributed hardware using DeepSpeed.
• Implement and experiment with DeepSpeed features for optimization and memory efficiency.

This instructor-led, live training in Ankara (online or onsite) is designed for beginner to intermediate-level developers who wish to utilize Large Language Models for various natural language tasks.

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

• Set up a development environment that includes a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Use LLMs for different 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, available online or onsite, is intended for 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 core principles of Stable Diffusion and its operation for image generation.
• Build and train Stable Diffusion models for image generation tasks.
• Apply Stable Diffusion to various image generation scenarios, including inpainting, outpainting, and image-to-image translation.
• Optimize the performance and stability of Stable Diffusion models.

In this instructor-led, live training in Ankara, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build 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 for solving complex problems.
• Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
• Push Python algorithms to their maximum potential.
• Use libraries and packages such as NumPy and Theano.

This course empowers programmers and data analysts with the essential techniques for constructing machine learning solutions from the ground up using Python. It explores 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. The curriculum reviews established practices for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. It enables professionals to implement effective ML models, assess algorithm constraints, and execute applied projects designed to solve real-world challenges.

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.

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts such as the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, along with aspects of communication, perception, and autonomous action. It guides executives and architects in evaluating AI-driven transformation opportunities, assessing emerging technology trends, and integrating practical intelligent solutions to enhance business agility.

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.

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 instructor-led live training in Ankara (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 Ankara (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.

This instructor-led live training in Ankara (online or onsite) is intended for researchers and developers who aim to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.

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

• Customize DeepMind Lab to build and run an environment that meets specific learning and training needs.
• Utilize DeepMind Lab's 3D simulation environment to train learning agents from a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

This instructor-led live training in Ankara (online or onsite) is designed for business analysts, data scientists, and developers who aim to build and implement deep learning models to accelerate revenue growth and solve problems in the business world.

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

• Understand the core concepts of machine learning and deep learning.
• Get insights on the future of business and industry with ML and DL.
• Define business strategies and solutions with deep learning.
• Learn how to apply data science and deep learning in solving business problems.
• Build deep learning models using Python, Pandas, TensorFlow, CNTK, Torch, Keras, etc.

This instructor-led, live training in Ankara (online or onsite) is aimed at data scientists who wish 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.
• Execute different CNN layers on the FPGA.
• Scale the application across multiple nodes in a Kubernetes cluster.

This instructor-led, live training in Ankara (online or onsite) targets data scientists aiming to use TensorFlow for analyzing potential fraud data.

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

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

This instructor-led, live training in Ankara (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.

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 Ankara (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

This instructor-led live training in Ankara (online or onsite) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

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

• Install and configure TensorFlow 2.x.
• Understand the benefits of TensorFlow 2.x over previous versions.
• Build deep learning models.
• Implement an advanced image classifier.
• Deploy a deep learning model to the cloud, mobile and IoT devices.

This course provides a comprehensive conceptual foundation in neural networks, machine learning algorithms, and deep learning (including algorithms and their practical applications).

Part 1 (40%) of the training focuses on fundamentals, assisting you in selecting the appropriate technology, such as TensorFlow, Caffe, Theano, DeepDrive, and Keras.

Part 2 (20%) introduces Theano, a Python library designed to simplify the creation of deep learning models.

Part 3 (40%) is extensively dedicated to TensorFlow, the open-source software library for Deep Learning developed by Google. All examples and hands-on exercises will utilize TensorFlow.

Audience

This course is designed for engineers looking to implement TensorFlow in their Deep Learning projects.

Upon completing this course, participants will:

• possess a solid understanding of Deep Neural Networks (DNN), CNNs, and RNNs
• comprehend TensorFlow's architecture and deployment mechanisms
• be capable of handling installation, production environment setup, architecture design, and configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production tasks, including model training, graph construction, and logging