Online Neural Networks Training in Kenya

This instructor-led live training in Kenya (online or onsite) is designed for experienced 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.

Applied AI from Scratch in Python empowers software developers and data analysts with essential techniques for constructing machine learning solutions from the ground up using Python. The course covers fundamental concepts such as supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network structures. It explores effective strategies for utilizing scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate practical AI development. Participants will learn to deploy functional ML models, assess algorithm constraints, and execute applied projects aimed at solving real-world challenges.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions through interaction with their environments. This technology drives numerous modern artificial intelligence advancements, including self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. By utilizing reward-based learning, DRL enables an artificial agent to learn strategies, optimize policies, and make autonomous decisions via trial and error.

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

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

• Grasp the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core 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 scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using contemporary tools.

Course Format

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

Customization Options

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

A deep dive into the fundamentals of artificial intelligence demonstrates how intelligent technologies are transforming digital strategies, automation processes, and decision-making frameworks within enterprise operations. This course examines core concepts including the history of AI, problem-solving methodologies, knowledge representation techniques, reasoning under uncertainty, and machine learning paradigms, alongside modules on communication, perception, and autonomous action. It equips executives and architects with the insights needed 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—specifically focusing on Machine Learning and Deep Learning—within the automotive sector. It guides learners in identifying technologies that can be effectively applied across various in-car scenarios, ranging from basic automation and image recognition to complex autonomous decision-making processes.

An Artificial Neural Network is a computational data model utilized in the creation of Artificial Intelligence (AI) systems capable of executing 'intelligent' tasks. Neural Networks are frequently employed in Machine Learning (ML) applications, which represent one form of AI implementation. Deep Learning constitutes a specialized subset of Machine Learning.

This instructor-led live training in Kenya (online or onsite) is tailored for researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring that the code is easy to debug.

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

• Set up the necessary development environment to start creating neural network models.
• Define and implement neural network models using clear and understandable source code.
• Run examples and modify existing algorithms to optimize deep learning training models, leveraging GPUs for high performance.

This instructor-led, live training in Kenya (online or onsite) offers an introduction to the fields of pattern recognition and machine learning. It covers 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 provides foundational knowledge on neural networks, machine learning algorithms, and deep learning (including algorithms and their applications).

Part-1 (40%) of this training focuses heavily on fundamentals while helping you select the appropriate technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2 (20%) of this training introduces Theano - a python library that simplifies the creation of deep learning models.

Part-3 (40%) of the training is heavily centered on Tensorflow - the API for Google's open-source software library for Deep Learning. The examples and hands-on exercises will all be conducted in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

Upon completing this course, delegates will:

• possess a solid understanding of deep neural networks (DNN), CNN, and RNN
• comprehend TensorFlow's structure and deployment mechanisms
• be capable of executing installation / production environment / architecture tasks and configuration
• be able to evaluate code quality, perform debugging, and monitoring
• be able to implement advanced production-like tasks such as training models, building graphs, and logging