Neural Networks Training in Central

This instructor-led, live training in Central (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.

Applied AI from Scratch in Python equips programmers and data analysts with foundational techniques for building machine learning solutions from the ground up using Python. Covers core principles of supervised learning classification and regression, unsupervised learning clustering and anomaly detection, and advanced neural network architectures. Examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks for hands-on AI development. Helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects for real-world problem solving.

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.

Exploring artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.

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.

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 Central (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 Central (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 begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part-2(20%) of this training introduces Theano - a python library that makes writing deep learning models easy.

Part-3(40%) of the training would be extensively based on Tensorflow - API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

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

After completing this course, delegates will:

• have a good understanding on deep neural networks(DNN), CNN and RNN
• 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, building graphs and logging