Neural Networks Basics

This course is designed to provide a comprehensive introduction to neural networks and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn the fundamental concepts, architectures, and training techniques of neural networks, enabling them to develop and deploy neural network models for various tasks relevant to electricity generation, renewable energy management, and utility operations, such as load forecasting, renewable energy output prediction, and fault detection.

• Understand the biological inspiration and mathematical foundations of neural networks

• Implement and train feedforward neural networks using Python and deep learning frameworks

• Apply backpropagation and gradient-based optimization techniques for neural network training

• Design and tune neural network architectures for specific Electricity Generation and Renewable Energy Plants & Utilities use cases

• Evaluate and interpret the performance of neural network models using appropriate metrics and techniques

1. Introduction to Neural Networks

• Historical context and biological inspiration behind neural networks

• Artificial neurons and activation functions

• Single-layer perceptrons and the XOR problem

• Hands-on exercises: Implementing a single-layer perceptron in Python

2. Feedforward Neural Networks

• Multi-layer perceptrons (MLPs) and their architecture

• Forward propagation and the universal approximation theorem

• Activation functions (sigmoid, tanh, ReLU) and their properties

• Hands-on exercises: Building and training MLPs using Python and NumPy

3. Training Neural Networks

• Cost functions and optimization objectives

• Backpropagation algorithm and gradient descent

• Stochastic gradient descent and mini-batch training

• Regularization techniques (L1/L2 regularization, dropout, early stopping)

• Hands-on exercises: Implementing backpropagation and training MLPs on electricity generation and renewable energy datasets

4. Neural Network Architectures and Hyperparameter Tuning

• Deeper architectures and their advantages

• Convolutional Neural Networks (CNNs) for analyzing satellite imagery and weather data

• Recurrent Neural Networks (RNNs) for time series data (e.g., load profiles, renewable energy output)

• Hyperparameter tuning and model selection techniques

• Hands-on exercises: Designing and tuning neural network architectures for Electricity Generation and Renewable Energy Plants & Utilities tasks

5. Applications and Advanced Topics

• Case studies of neural networks in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., load forecasting, renewable energy output prediction, fault detection)

• Unsupervised learning with neural networks (autoencoders, self-organizing maps)

• Introduction to deep learning frameworks (TensorFlow, PyTorch)

• Hands-on exercises: Developing a neural network model for a specific Electricity Generation or Renewable Energy Plants & Utilities use case

• 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