Udemy – A deep understanding of AI large language model mechanisms 2025-6

Udemy – A deep understanding of AI large language model mechanisms 2025-6 Downloadly IRSpace

Aug 18, 2025 - 15:53 Updated: Aug 19, 2025 - 14:42

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Udemy – A deep understanding of AI large language model mechanisms 2025-6

A deep understanding of AI large language model mechanisms. This intensive course, with over 90 hours of instruction, takes a close look at large language models (LLMs) such as ChatGPT, GPT-4, GPT-5, Claude, Gemini, and LLaMA. Unlike typical courses that only teach the application of these models, this course provides an in-depth analysis of their mechanisms using machine learning methods and mechanical interpretation capabilities. Topics include transformer architecture, self-attention mechanisms, embeddings, training pipelines, and inference strategies, presented with practical Python and PyTorch code. This course is designed for researchers, engineers, and advanced learners who want to go beyond using the API as a “black box.” Participants will learn the full architecture of LLMs, the mathematics of attention mechanisms, the training process, fine-tuning, prompt engineering, evaluation metrics, and inference techniques. It also examines the limitations and biases of LLMs, interpretability, ethical considerations, and responsible AI. Practical implementations with PyTor, principal component analysis, high-dimensional clustering, and advanced applications of cosine similarity are also covered. This course provides a comprehensive foundation for building transformers from scratch, tuning existing models, or understanding the mathematics and engineering behind generative AI.

What you will learn

Large Language Model (LLM) architectures, including GPT (from OpenAI) and BERT.
Transformer blocks.
Attention algorithm.
PyTorch.
LLM pre-training.
Explainable AI.
Mechanistic Interpretability.
Machine learning.
Deep learning.
Principal components analysis.
High-dimensional clustering.
Dimension reduction.
Advanced applications of cosine similarity.

This course is suitable for people who:

Machine learning engineers and data scientists.
Artificial intelligence researchers and natural language processing experts.
Software developers interested in deep learning and generative artificial intelligence.
Graduate students or self-taught learners with intermediate Python skills and basic machine learning knowledge.
Artificial intelligence engineers.
Scientists interested in modern autoregressive modeling.
Natural language processing enthusiasts.
Students taking a machine learning or data science course.
Undergraduate students interested in large language models.
Machine learning or data science specialists.
Researchers in the field of explainable artificial intelligence.