Deep Learning Training in Maia

This instructor-led live training in Maia (online or onsite) targets intermediate-level developers, data scientists, and AI practitioners who aim to leverage TensorFlow Lite for Edge AI applications.

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

• Grasp 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 Maia (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 Maia (online or onsite) is aimed at 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:

• 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 Maia (online or onsite) is aimed at advanced-level professionals who wish to specialise 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 Maia (online or onsite) is targeted at advanced-level professionals who wish to investigate cutting-edge 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 Maia (online or onsite) is aimed at intermediate to advanced-level 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:

• 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 Maia (online or onsite) is designed for advanced professionals aiming to leverage AI techniques to revolutionise drug discovery and development processes.

Upon completion 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 Maia (online or onsite) is designed for data scientists and machine learning engineers at the beginner to intermediate levels who seek to improve 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 Maia (online or onsite) is designed for developers at beginner to intermediate levels who wish to utilize Large Language Models for various natural language tasks.

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

• Configure a development environment integrated with a prominent LLM.
• Construct a foundational LLM and fine-tune it using a custom dataset.
• Apply LLMs to various natural language tasks such as text summarization, question answering, and text generation.
• Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

This instructor-led, live training in (online or onsite) is aimed at 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 principles of Stable Diffusion and how it works 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.

In this instructor-led, live training in Maia, attendees will master the most pertinent and state-of-the-art machine learning techniques in Python while developing a range 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 address complex problems.
• Apply deep learning and semi-supervised learning methods to applications involving image, music, text, and financial data.
• Maximize the potential of Python algorithms.
• Utilize libraries and packages such as NumPy and Theano.

Applied AI from Scratch in Python empowers programmers and data analysts with the essential techniques required to build machine learning solutions from the ground up using Python. The course covers the core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. It examines proven methodologies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. Participants will learn to implement practical ML models, assess algorithm limitations, and complete applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through environmental interaction. This technology drives numerous contemporary AI innovations, including autonomous vehicles, robotic control systems, algorithmic trading, and adaptive recommendation engines. By utilizing reward-based learning, DRL enables artificial agents to learn strategies, optimize policies, and execute autonomous decisions via trial and error.

This instructor-led live training, available online or onsite, is designed for intermediate-level developers and data scientists seeking to master and apply Deep Reinforcement Learning techniques to develop intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of 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 to practical applications, including games, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance with modern tools.

Format of the Course

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

Course Customization Options

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

An exploration of artificial intelligence fundamentals demonstrates how intelligent technology transforms digital strategy, automation, and decision-making processes within enterprise operations. The course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside aspects of communication, perception, and autonomous action. It equips executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

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

Artificial Neural Networks are computational data models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as one of the primary implementations of AI. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led, live training in Maia (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 Maia (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 Maia (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.

This instructor-led live training in Maia (online or on-site) is intended for business analysts, data scientists, and developers who wish to build and implement deep learning models to accelerate revenue growth and solve business world problems.

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 Maia (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 Maia (online or onsite) is designed for data scientists who wish to utilise TensorFlow to analyse 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 analysing fraud data.

This instructor-led, live training in Maia (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.

During this instructor-led live training, participants will discover how to utilize Matlab to design, construct, and visualize a convolutional neural network specifically for image recognition.

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

• Construct a deep learning model
• Automate the data labeling process
• Utilize models from Caffe and TensorFlow-Keras
• Train data employing multiple GPUs, cloud resources, or clusters

Audience

• Developers
• Engineers
• Domain experts

Course Format

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

This instructor-led, live training in Maia (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 quickly 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 Maia (online or onsite) is intended for developers and data scientists who wish to utilize TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and similar 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 a foundational understanding of neural networks, machine learning algorithms, and deep learning (both algorithms and applications).

Part 1 (40%) of this training focuses on fundamentals, helping you select the appropriate technology such as TensorFlow, Caffe, Theano, DeepDrive, Keras, and others.

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

Part 3 (40%) of the training is extensively based on TensorFlow, Google's open-source software library for deep learning. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

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

Upon completion, participants will be able to:

• Gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs
• Comprehend TensorFlow’s structure and deployment mechanisms
• Perform installation, production environment setup, architecture tasks, and configuration
• Assess code quality, debug, and monitor systems
• Implement advanced production-like tasks such as training models, building graphs, and logging