Online Deep Learning Training in Portugal

This instructor-led, live training in Portugal (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 Portugal (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 Portugal (online or onsite) is aimed at beginner to intermediate-level data scientists and machine learning engineers who wish to improve the performance of their deep learning models.

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 Portugal (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use 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.

In this instructor-led, live training in Portugal, 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 is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. 

Deep Reinforcement Learning (DRL) combines reinforcement learning principles with deep learning architectures to enable agents to make decisions through interaction with their environments. It underpins many modern AI advancements such as self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial and error using reward-based learning.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.

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

• Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement key RL algorithms including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
• Apply DRL to real-world applications such as games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

• Interactive lecture and guided discussion.
• Hands-on exercises and practical implementations.
• Live coding demonstrations and project-based applications.

Course Customization Options

• To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

In this instructor-led, live training in Portugal, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning.
• Learn the applications and uses of deep learning in telecom.
• Use Python, Keras, and TensorFlow to create deep learning models for telecom.
• Build their own deep learning customer churn prediction model using Python.

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.

This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course. 

This instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level data scientists and statisticians who wish to prepare data, build models, and apply machine learning techniques effectively in their professional domains.

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

• Understand and implement various Machine Learning algorithms.
• Prepare data and models for machine learning applications.
• Conduct post hoc analyses and visualize results effectively.
• Apply machine learning techniques to real-world, sector-specific scenarios.

Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.

This instructor-led, live training in Portugal (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code 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 a 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 Portugal (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 is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.

This instructor-led, live training in Portugal (online or onsite) is aimed at researchers and developers who wish 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 suits learning and training needs.
• Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
• Facilitate agent evaluation to develop intelligence in a 3D game-like world.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.

This instructor-led, live training in Portugal (online or onsite) is aimed at business analysts, data scientists, and developers who wish 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.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

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

• Understand the fundamental concepts of deep learning
• Learn the applications and uses of deep learning in banking
• Use Python, Keras, and TensorFlow to create deep learning models for banking
• Build their own deep learning credit risk model using Python

Audience

• Developers
• Data scientists

Format of the course

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

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source) for analyzing computer images

This course provide working examples.

This instructor-led, live training in Portugal (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, 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 Portugal (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to develop their understanding of machine learning algorithms, deep learning techniques, and AI-driven decision-making. The course provides hands-on experience with machine learning concepts, deep learning models, and practical implementations using R.

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

• Understand the fundamentals of machine learning and deep learning.
• Apply various machine learning algorithms for regression, classification, clustering, and anomaly detection.
• Use deep learning architectures such as artificial neural networks (ANNs).
• Implement supervised and unsupervised learning models.
• Evaluate model performance and optimize hyperparameters.
• Use R for data analysis, visualization, and machine learning applications.

The training is aimed at people who want to learn the basics of neural networks and their applications.

This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries

This instructor-led, live training in Portugal (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.

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

This instructor-led, live training in Portugal (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

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

• Install TensorFlow Lite.
• Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
• Add AI to hardware devices without relying on network connectivity.

This instructor-led, live training in Portugal (online or onsite) is aimed at data scientists who wish to go from training a single ML model to deploying many ML models to production.

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

• Install and configure TFX and supporting third-party tools.
• Use TFX to create and manage a complete ML production pipeline.
• Work with TFX components to carry out modeling, training, serving inference, and managing deployments.
• Deploy machine learning features to web applications, mobile applications, IoT devices and more.

TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

• 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, embedding terms, building graphs and logging