EE401 — Deep Learning, LLMs and Generative AI for Energy

From Neural Networks to Generative AI

EE401 is the second Core course in GIEE's AI in Energy curriculum. Building on the machine learning foundation established in EE400, this course takes engineers into the deep learning architectures that power most modern AI applications, including the large language models and generative AI systems transforming the energy sector today.

You will move beyond "deep learning is a black box" to genuinely understanding how neural networks learn, why transformers enabled the LLM revolution, and where each architecture fits in real energy applications. The course covers the technical mechanics in enough depth that you can engage productively with vendors, evaluate AI proposals, and reason about model selection — without requiring you to become a deep learning researcher.

EE401 is required for both CAGP (Certified AI for Grid Professional) and CAEP (Certified AI for Energy Professional) certifications, and provides the deep-learning foundation for prompt engineering (EE403), agentic AI (EE404), and the specialized Add-On courses.

What You Will Learn

• Build mental models of how neural networks learn through gradient descent and backpropagation
• Match neural network architectures to data: CNN for images, LSTM for time series, transformers for text and beyond
• Explain how large language models work and where their capabilities and limitations come from
• Apply transfer learning and fine-tuning effectively for energy domain adaptations
• Distinguish when to use generative AI methods (GANs, VAEs, diffusion models) for energy problems
• Reason about the fine-tuning versus RAG decision matrix for enterprise energy applications
• Evaluate the technical claims and capabilities of modern AI vendors with informed judgment
• Design appropriate evaluation frameworks for deep learning models in energy contexts

Course Structure

EE401 is organized into four modules covering the deep learning landscape from foundations through frontier applications:

• Module 1: Neural Network Foundations — How neural networks actually learn. Activation functions, depth versus width, backpropagation, loss functions, and modern training techniques.
• Module 2: Specialized Architectures — CNN for image data, RNN and LSTM for sequential data, autoencoders for anomaly detection, with energy applications throughout.
• Module 3: Transformers and Large Language Models — Attention mechanisms, transformer architecture, how LLMs are trained, and what makes models like GPT and Claude work.
• Module 4: Generative AI for Energy — GANs, VAEs, diffusion models, and the practical decision framework for fine-tuning versus prompt engineering versus RAG.

Real-World Examples

Every concept is grounded in energy applications. Learn LSTM through utility load forecasting where a deep learning model achieved 1.5% MAPE versus 5-7% for linear baselines. Learn attention mechanisms by exploring how transformers process maintenance work orders. Learn GANs through synthetic load profile generation for rare fault scenarios. Learn VAE-based anomaly detection in SCADA data using reconstruction error. Real numbers, real architectures, real engineering decisions.

Who This Course Is For

• Engineers who completed EE400 and want to go deeper into modern AI architectures
• Practitioners evaluating AI vendors and proposals for utility or energy applications
• Engineering managers building AI literacy to lead technical teams
• Anyone pursuing CAGP or CAEP certification
• Engineers who want to engage credibly with data science and AI specialist colleagues

Prerequisites

• EE400 — AI and Machine Learning Fundamentals for Energy (strongly recommended; concepts build directly on EE400 foundations)
• Engineering or technical background
• Comfort with basic mathematics (matrix concepts and basic calculus intuition help, but are not required at a manipulative level)
• No prior deep learning experience required

Format and Access

• Duration: Approximately 12 hours of content
• Format: Self-paced online with video instruction, demonstrations, and quizzes
• Course Access: 6 months of full access from enrollment
• Completion Window: 90 days to complete coursework and the final exam
• Assessment: 4 module quizzes (30% of grade) + comprehensive final exam (70% of grade)
• Passing Score: 70% overall
• Language: English
• AI Tools: Encouraged for learning and exercises; prohibited during quizzes and the final exam

Path to Certification

EE401 is the second course in GIEE's AI in Energy Core curriculum and contributes to two professional certifications:

• CAGP — Certified AI for Grid Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Grid Add-On courses (EE410, EE411, EE412), then pass the CAGP certification exam.
• CAEP — Certified AI for Energy Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Energy Add-On courses (EE420, EE421, EE422), then pass the CAEP certification exam.