Neural Networks Training in Abu Dhabi

This instructor-led, live training in Abu Dhabi (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building 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 four-day course provides an introduction to artificial intelligence and its applications through the use of the Python programming language. Participants also have the option to extend their learning by spending an extra day working on an AI project upon completing the course.

Deep Reinforcement Learning involves an "artificial agent" learning through trial-and-error and reward-based feedback. This artificial entity aims to mimic human capabilities in acquiring and constructing knowledge independently from raw inputs like visual data. Deep reinforcement learning leverages deep learning techniques and neural networks, distinguishing itself from traditional machine learning methods that depend on supervised or unsupervised approaches.

This instructor-led training (conducted online or in-person) is designed for developers and data scientists interested in mastering the basics of Deep Reinforcement Learning while creating a Deep Learning Agent.

Upon completion, participants will be able to:

• Grasp the core principles of Deep Reinforcement Learning and differentiate it from Machine Learning.
• Utilize advanced Reinforcement Learning algorithms to address practical challenges.
• Construct a Deep Learning Agent.

Course Format

• Engaging lectures and discussions.
• Absorbing exercises and ample practice opportunities.
• Practical implementation in a live-lab setting.

Customization Options for the Course

• To arrange a customized training session, please contact us to discuss your requirements.

This course is designed for managers, solutions architects, innovation officers, CTOs, software architects, and anyone else interested in gaining an understanding of practical artificial intelligence applications and predictions for its future growth.

This course delves into AI, with a focus on Machine Learning and Deep Learning, within the context of the automotive industry. It aids in identifying which technologies could potentially be applied across various scenarios in a vehicle, ranging from basic automation and image recognition to advanced autonomous decision-making.

This instructor-led, live training in Abu Dhabi (online or onsite) is aimed at beginner-level participants who wish to learn essential concepts in probability, statistics, programming, and machine learning, and apply these to AI development.

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

• Understand basic concepts in probability and statistics, and apply them to real-world scenarios.
• Write and understand procedural, functional, and object-oriented programming code.
• Implement machine learning techniques such as classification, clustering, and neural networks.
• Develop AI solutions using rules engines and expert systems for problem-solving.

An Artificial Neural Network is a computational model utilized in the creation of Artificial Intelligence (AI) systems designed to execute "intelligent" tasks. These networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning falls under the broader category of Machine Learning.

This four-day course provides an introduction to artificial intelligence and its applications. Participants also have the option to extend their learning with an extra day dedicated to working on an AI project upon finishing the course.

This instructor-led, live training in Abu Dhabi (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.

This instructor-led, live training in Abu Dhabi (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 Abu Dhabi (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.

In this instructor-led live training session, participants will gain skills in utilizing Matlab to create, construct, and visualize a convolutional neural network for image recognition.

Upon completion of the course, participants will be equipped with the ability to:

• Create a deep learning model
• Automate the process of data labeling
• Operate with models from Caffe and TensorFlow-Keras
• Train datasets using multiple GPUs, cloud services, or clusters

Audience

• Software Developers
• Engineers
• Subject Matter Experts

Course Format

• The course includes lectures, discussions, practical exercises, and extensive hands-on practice.

This training is designed for individuals interested in mastering the fundamentals of neural networks and exploring their various applications.

This classroom-based training session will include presentations and computer-based examples, along with case study exercises that utilize pertinent neural and deep learning network libraries.

This course provides an overview of utilizing neural networks to solve practical problems through the use of R-project software.

In this instructor-led, live training in Abu Dhabi, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

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

• Train various types of neural networks on large amounts of data.
• Use TPUs to speed up the inference process by up to two orders of magnitude.
• Utilize TPUs to process intensive applications such as image search, cloud vision and photos.

This course starts by providing you with a conceptual understanding of neural networks and machine learning algorithms, including deep learning (algorithms and applications).

The first part (40%) of this training focuses on fundamentals but will assist you in selecting the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

The second part (20%) introduces Theano—a Python library that simplifies writing deep learning models.

The third part (40%) of the training is heavily based on TensorFlow—the second-generation API of Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Target Audience

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

Upon completion of this course, participants will:

• have a solid understanding of deep neural networks (DNN), CNN, and RNN

• understand the structure and deployment mechanisms of TensorFlow

• be capable of performing installation, setting up production environments, configuring architecture tasks

• assess code quality, perform debugging, and monitoring

• implement advanced production tasks such as training models, building graphs, and logging