Deep Understanding of Large Language Models (LLMs): Architecture, Training, and Mechanisms

Description

Large Language Models (LLMs) like ChatGPT, GPT-4, , GPT5, Claude, Gemini, and LLaMA are transforming artificial intelligence, natural language processing (NLP), and machine learning. But most courses only teach you how to use LLMs. This 90+ hour intensive course teaches you how they actually work — and how to dissect them using machine-learning and mechanistic interpretability methods.

This is a deep, end-to-end exploration of transformer architectures, self-attention mechanisms, embeddings layers, training pipelines, and inference strategies — with hands-on Python and PyTorch code at every step.

Whether your goal is to build your own transformer from scratch, fine-tune existing models, or understand the mathematics and engineering behind state-of-the-art generative AI, this course will give you the foundation and tools you need.

What You’ll Learn

• The complete architecture of LLMs — tokenization, embeddings, encoders, decoders, attention heads, feedforward networks, and layer normalization
• Mathematics of attention mechanisms — dot-product attention, multi-head attention, positional encoding, causal masking, probabilistic token selection
• Training LLMs — optimization (Adam, AdamW), loss functions, gradient accumulation, batch processing, learning-rate schedulers, regularization (L1, L2, decorrelation), gradient clipping
• Fine-tuning and prompt engineering for downstream NLP tasks, system-tuning
• Evaluation metrics — perplexity, accuracy, and benchmark datasets such as MAUVE, HellaSwag, SuperGLUE, and ways to assess bias and fairness
• Practical PyTorch implementations of transformers, attention layers, and language model training loops, custom classes, custom loss functions
• Inference techniques — greedy decoding, beam search, top-k sampling, temperature scaling
• Scaling laws and trade-offs between model size, training data, and performance
• Limitations and biases in LLMs — interpretability, ethical considerations, and responsible AI
• Decoder-only transformers
• Embeddings, including token embeddings and positional embeddings
• Sampling techniques — methods for generating new text, including top-p, top-k, multinomial, and greedy

Why This Course Is Different

• 93+ hours of HD video lectures — blending theory, code, and practical application
• Code challenges in every section — with full, downloadable solutions
• Builds from first principles — starting from basic Python/Numpy implementations and progressing to full PyTorch LLMs
• Suitable for researchers, engineers, and advanced learners who want to go beyond “black box” API usage
• Clear explanations without dumbing down the content — intensive but approachable

Who Is This Course For?

• Machine learning engineers and data scientists
• AI researchers and NLP specialists
• Software developers interested in deep learning and generative AI
• Graduate students or self-learners with intermediate Python skills and basic ML knowledge
• Technologies & Tools Covered
• Python and PyTorch for deep learning
• NumPy and Matplotlib for numerical computing and visualization
• Google Colab for free GPU access
• Hugging Face Transformers for working with pre-trained models
• Tokenizers and text preprocessing tools
• Implement Transformers in PyTorch, fine-tune LLMs, decode with attention mechanisms, and probe model internals
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What if you have questions about the material?

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

By the end of this course, you won’t just know how to work with LLMs — you’ll understand why they work the way they do, and be able to design, train, evaluate, and deploy your own transformer-based language models.

Enroll now and start mastering Large Language Models from the ground up.