Deep Learning Training in Oslo

This live, instructor-led training, conducted in Oslo (either online or onsite), is designed for intermediate-level developers, data scientists, and AI professionals seeking to apply TensorFlow Lite for Edge AI solutions.

By the conclusion of this training, participants will be capable of:

• Comprehending the basics of TensorFlow Lite and its function in Edge AI.
• Developing and optimizing AI models via TensorFlow Lite.
• Deploying TensorFlow Lite models onto various edge devices.
• Applying tools and methods for model conversion and optimization.
• Implementing functional Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Oslo (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 live, instructor-led training in Oslo (online or onsite) targets intermediate-level data scientists and developers eager to understand and apply deep learning techniques within the Google Colab ecosystem.

By the conclusion 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 Oslo (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 Oslo (online or onsite) is designed for advanced professionals eager to investigate cutting-edge XAI techniques for deep learning models, with a strong emphasis on developing interpretable AI systems.

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

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

This instructor-led, live training in Oslo (online or onsite) is designed for intermediate to advanced data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish 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:

• Comprehend advanced deep learning architectures and methodologies for text-to-image generation.
• Deploy complex models and optimization strategies to achieve high-fidelity image synthesis.
• Enhance performance and scalability when working with large datasets and intricate models.
• Refine hyperparameters to improve model performance and generalization capabilities.
• Seamlessly integrate Stable Diffusion with other deep learning frameworks and tools.

This instructor-led, live training in Oslo (online or onsite) is designed for advanced professionals seeking to utilize AI techniques to transform drug discovery and development processes.

By the conclusion 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 Oslo (online or on-site) 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 Oslo (online or onsite) targets developers with beginner to intermediate skills who wish to apply Large Language Models to various natural language tasks.

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

• Set up a development environment equipped with a popular LLM.
• Create a basic LLM and fine-tune it on a custom dataset.
• Utilize LLMs for different natural language tasks, including text summarization, question answering, and text generation.
• Debug and assess LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training (available online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers looking to leverage Stable Diffusion for generating high-quality images across various use cases.

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

• Understand the principles of Stable Diffusion and its operational logic 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.

During this instructor-led, live training in Oslo, participants will master the most relevant and cutting-edge machine learning techniques in Python by developing a series of demo applications that handle image, music, text, and financial data.

Upon completion 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.

The course Applied AI from Scratch in Python empowers programmers and data analysts with the foundational techniques required to build machine learning solutions from the ground up using Python. It covers core 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 examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to implement functional ML models, assess algorithmic limitations, and complete applied projects designed for solving real-world problems.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions by interacting with their environments. This technology drives many contemporary AI innovations, 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 through trial-and-error mechanisms based on reward signals.

This instructor-led live training is available both online and onsite. It is designed for intermediate-level developers and data scientists who want to master and apply Deep Reinforcement Learning techniques to create intelligent agents capable of making autonomous decisions in complex settings.

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

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Implement essential 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 applications like gaming, robotics, and decision optimization.
• Use modern tools to troubleshoot, visualize, and optimize training performance.

Format of the Course

• Interactive lectures accompanied by guided discussions.
• Practical exercises and hands-on implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (for example, using PyTorch instead of TensorFlow), please contact us to make arrangements.

Delving into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making capabilities within enterprise operations. This course examines essential concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside topics such as communication, perception, and autonomous action. It empowers executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technological trends, and implement practical intelligent solutions to enhance business agility.

This course explores the application of AI, with a focus on Machine Learning and Deep Learning, within the automotive sector. It guides participants in identifying suitable technologies for various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making systems.

An Artificial Neural Network is a computational data model employed in creating Artificial Intelligence (AI) systems that can execute "intelligent" tasks. Neural Networks are widely utilized in Machine Learning (ML) applications, which serve as a specific implementation of AI. Deep Learning represents a specialized subset of Machine Learning.

This instructor-led, live training in Oslo (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 in Oslo (available online or onsite) targets software developers, data analysts, and technical professionals who intend to use TensorFlow 2.x and Keras to build, train, and deploy deep learning models for computer vision, natural language processing, and multimodal applications.

This instructor-led live training in Oslo (online or on-site) targets data scientists who intend to use TensorFlow to analyze 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.

Through this instructor-led, live training session, participants will acquire the skills to design, construct, and visualize convolutional neural networks for image recognition using Matlab.

Upon completion of this training, participants will be capable of:

• Constructing deep learning models
• Automating the data labeling process
• Utilizing models from Caffe and TensorFlow-Keras
• Training data across multiple GPUs, cloud environments, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• A combination of lectures, discussions, exercises, and extensive hands-on practice

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

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 initiates with a conceptual foundation in neural networks, covering broad aspects of machine learning algorithms and deep learning (including algorithms and their applications).

Part-1 (40%) of this training concentrates on fundamentals, aiding you in selecting the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

Part-3 (40%) of the training is heavily focused on TensorFlow, the API for Google's open-source deep learning software library. All examples and hands-on exercises will utilize TensorFlow.

Audience

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

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow's structure and deployment mechanisms
• be capable of handling installation, production environment setup, architecture tasks, and configuration
• be able to assess code quality, perform debugging, and monitor systems
• be able to implement advanced production tasks such as training models, building graphs, and logging