Neural Networks Basics

This course dives into the fascinating world of Neural Networks, a fundamental building block of Artificial Intelligence (AI). You'll explore the core concepts behind these powerful models and their potential to revolutionize Supply Chain Management (SCM) by enabling data-driven decision making, optimization, and predictive capabilities.

• Define Neural Networks and their core principles (inspired by the human brain).

• Understand the structure of a neural network (neurons, layers, activation functions).

• Explore different types of neural networks (Perceptrons, Multi-Layer Perceptrons, Convolutional Neural Networks).

• Identify real-world applications of neural networks in Supply Chain Management (e.g., demand forecasting, inventory optimization, anomaly detection).

• Gain insights into the training process of neural networks (supervised learning, backpropagation).

1. Unveiling Neural Networks

• Introduction to Neural Networks: Mimicking the human brain for intelligent computing.

• Demystifying the Building Blocks: Neurons, activation functions, and understanding how information flows through the network.

• Exploring different network architectures: Perceptrons, limitations, and the power of Multi-Layer Perceptrons (MLPs) with hidden layers.

• Hands-on Exercises: Utilizing online tools or simple coding exercises to visualize basic neural network functionalities.

• Case Studies: Exploring early applications of neural networks in demand forecasting and pattern recognition for SCM tasks.

2. Applications in SCM and Beyond

• Introduction to Convolutional Neural Networks (CNNs): Specialized networks for image analysis with applications in SCM.

• Understanding how CNNs process images (filters, pooling layers) for tasks like product defect detection or automated visual inspection.

• Training Neural Networks: Supervised learning, backpropagation algorithm, and optimizing network performance for SCM tasks.

• Exploring the potential of neural networks for anomaly detection in sensor data and predictive maintenance in supply chains.

• Course Wrap-up: Addressing limitations of neural networks and responsible AI practices in SCM implementations.

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and libraries such as NumPy and Matplotlib

• Familiarity with basic machine learning concepts and techniques