Deep Learning Training in Stockholm

This instructor-led live training in Stockholm (online or onsite) is intended for intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI applications.

By the conclusion 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 Stockholm (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 Stockholm (online or on-site) is designed for intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.

Upon completion 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 Stockholm (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 Stockholm (online or onsite) is designed for advanced professionals who wish to explore state-of-the-art XAI techniques for deep learning models, focusing on the development of interpretable AI systems.

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

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

This instructor-led live training in Stockholm (online or onsite) targets intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts looking 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 Stockholm (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 Stockholm (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 Stockholm (online or onsite) is designed for beginner to intermediate-level developers who wish to utilize Large Language Models for a variety of 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 (online or onsite) is designed for data scientists, machine learning engineers, and computer vision researchers who want to leverage Stable Diffusion to create high-quality images for diverse use cases.

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

• Understand the core principles of Stable Diffusion and its image generation mechanism.
• Build and train Stable Diffusion models for image creation 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 Stockholm, 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 developers and data analysts with the essential techniques required to construct 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 structures. Participants will examine proven strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. The program supports professionals in deploying effective ML models, assessing algorithm constraints, and delivering applied projects tailored to real-world challenges.

Deep Reinforcement Learning (DRL) integrates the core 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 contemporary AI breakthroughs, including self-driving cars, robotic control systems, algorithmic trading, and adaptive recommendation engines. DRL enables artificial agents to acquire strategies, refine policies, and execute autonomous decisions through a process of trial and error driven by reward mechanisms.

This instructor-led live training (available online or onsite) is designed for intermediate-level developers and data scientists who aim to master and apply Deep Reinforcement Learning techniques to construct intelligent agents capable of making autonomous decisions within complex environments.

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

• Grasp the theoretical foundations and mathematical concepts underlying Reinforcement Learning.
• Execute essential RL algorithms, such as Q-Learning, Policy Gradients, and Actor-Critic methods.
• Develop and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Deploy DRL solutions in real-world scenarios, including gaming, robotics, and decision optimization.
• Diagnose, visualize, and enhance training performance using contemporary tools.

Course Format

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

Customization Options

• To request a tailored version of this course (for instance, substituting PyTorch for TensorFlow), please get in touch with us to make arrangements.

Delving into the fundamentals of artificial intelligence uncovers how intelligent technologies are reshaping digital strategies, automation, and decision-making processes within enterprise operations. This exploration covers core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and various machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and integrate practical intelligent solutions to enhance business agility.

This course explores the application of AI—focusing on Machine Learning and Deep Learning—within the automotive sector. It guides participants in identifying which technologies can be (potentially) applied across various in-car scenarios, ranging from basic automation and image recognition to autonomous decision-making.

Artificial Neural Networks are computational models used to develop Artificial Intelligence (AI) systems that can perform "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which represent one form of AI implementation, while Deep Learning constitutes a specific subset of Machine Learning.

This instructor-led, live training in Stockholm (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 Stockholm (online or onsite) is aimed at data scientists who wish 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.

In this instructor-led, live training session, participants will learn how to use MATLAB to design, build, and visualize a convolutional neural network (CNN) 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, conducted at Stockholm (online or on-site), is intended for developers and data scientists who aim to utilize TensorFlow 2.x to develop predictors, classifiers, generative models, neural networks, and related applications.

By the conclusion 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 you with a solid conceptual foundation in neural networks, covering machine learning algorithms, deep learning methodologies, and their practical applications.

Part 1 (40%) of the training focuses heavily on fundamental principles, empowering you to select the most appropriate technology for your needs, such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

Part 3 (40%) is extensively dedicated to TensorFlow, Google's open-source software library for Deep Learning. All examples and hands-on exercises will be conducted within the TensorFlow environment.

Audience

This course is designed for engineers who wish to utilize TensorFlow for their Deep Learning projects.

Upon completion of this course, participants will:

• possess a comprehensive understanding of deep neural networks (DNN), CNNs, and RNNs
• understand TensorFlow’s architecture and deployment mechanisms
• be capable of performing installation, production environment setup, and architectural configuration
• be able to assess code quality, perform debugging, and monitor performance
• be able to implement advanced production-level tasks, such as training models, constructing graphs, and managing logs