Neural Networks Training in Puebla

This instructor-led, live training in Puebla (online or onsite) targets advanced-level professionals who wish to explore cutting-edge XAI techniques for deep learning models, focusing on the creation 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.

The Applied AI from Scratch in Python course provides programmers and data analysts with the fundamental skills required to build machine learning solutions from the ground up using Python. The curriculum covers core principles of supervised learning (classification and regression), unsupervised learning (clustering and anomaly detection), and advanced neural network architectures. Participants will examine proven methods for leveraging scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. This course empowers professionals to implement practical ML models, assess algorithmic limitations, and execute applied projects designed for real-world problem solving.

Deep Reinforcement Learning (DRL) merges reinforcement learning principles with deep learning architectures, empowering agents to make decisions via interaction with their surroundings. This technology drives numerous contemporary AI breakthroughs, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. Through reward-based learning, a DRL agent can learn strategies, optimize policies, and execute autonomous decisions through trial and error.

This instructor-led live training (available online or on-site) targets intermediate developers and data scientists eager to master and apply Deep Reinforcement Learning techniques to develop intelligent agents capable of making autonomous decisions in complex environments.

Upon completing this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles of 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 scenarios like gaming, robotics, and decision optimization.
• Utilize modern tools to troubleshoot, visualize, and optimize training performance.

Course Format

• Interactive lectures and 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 (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.

Uncovering the fundamentals of artificial intelligence reveals how intelligent technologies are transforming digital strategies, automation, and decision-making processes across enterprise operations. This content examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, along with communication, perception, and autonomous action. It guides executives and architects in evaluating opportunities for AI-driven transformation, assessing emerging technology trends, and integrating practical intelligent solutions to accelerate business agility.

This course examines the application of AI—specifically Machine Learning and Deep Learning—within the automotive sector. It guides learners in identifying which technologies can be effectively applied across various vehicle scenarios, ranging from basic automation and image recognition to autonomous decision-making.

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 Puebla (online or onsite) targets researchers and developers who wish to use Chainer to build and train neural networks in Python, ensuring 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 developing neural network models.
• Define and implement neural network models using 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 Puebla (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 starts by providing conceptual knowledge of neural networks, as well as a broader understanding of machine learning algorithms and deep learning (including both algorithms and their applications).

Part-1 (40%) of this training focuses primarily on fundamentals, yet it will assist you in selecting 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. All examples and hands-on exercises will be conducted using TensorFlow.

Audience

This course is designed for engineers who intend to utilize TensorFlow for their Deep Learning projects.

Upon completing this course, delegates will:

• gain a solid understanding of deep neural networks (DNN), CNNs, and RNNs.
• comprehend TensorFlow’s structure and deployment mechanisms.
• be capable of performing installation, production environment setup, architecture tasks, and configuration.
• be able to evaluate code quality, perform debugging, and monitor performance.
• be able to implement advanced production-level tasks such as training models, building graphs, and logging.