A deep understanding of AI large language model mechanisms

<div>Deep Understanding of Large Language Models (LLMs): Architecture, Training, and Mechanisms</div><div><br></div><div><span style="font-size: 1rem;">Description</span></div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div><br></div><div>What You’ll Learn</div><div><ul><li><span style="font-size: 1rem;">The complete architecture of LLMs — tokenization, embeddings, encoders, decoders, attention heads, feedforward networks, and layer normalization</span></li><li><span style="font-size: 1rem;">Mathematics of attention mechanisms — dot-product attention, multi-head attention, positional encoding, causal masking, probabilistic token selection</span></li><li><span style="font-size: 1rem;">Training LLMs — optimization (Adam, AdamW), loss functions, gradient accumulation, batch processing, learning-rate schedulers, regularization (L1, L2, decorrelation), gradient clipping</span></li><li><span style="font-size: 1rem;">Fine-tuning and prompt engineering for downstream NLP tasks, system-tuning</span></li><li><span style="font-size: 1rem;">Evaluation metrics — perplexity, accuracy, and benchmark datasets such as MAUVE, HellaSwag, SuperGLUE, and ways to assess bias and fairness</span></li><li><span style="font-size: 1rem;">Practical PyTorch implementations of transformers, attention layers, and language model training loops, custom classes, custom loss functions</span></li><li><span style="font-size: 1rem;">Inference techniques — greedy decoding, beam search, top-k sampling, temperature scaling</span></li><li><span style="font-size: 1rem;">Scaling laws and trade-offs between model size, training data, and performance</span></li><li><span style="font-size: 1rem;">Limitations and biases in LLMs — interpretability, ethical considerations, and responsible AI</span></li><li><span style="font-size: 1rem;">Decoder-only transformers</span></li><li><span style="font-size: 1rem;">Embeddings, including token embeddings and positional embeddings</span></li><li><span style="font-size: 1rem;">Sampling techniques — methods for generating new text, including top-p, top-k, multinomial, and greedy</span></li></ul></div><div><br></div><div>Why This Course Is Different</div><div><ul><li><span style="font-size: 1rem;">93+ hours of HD video lectures — blending theory, code, and practical application</span></li><li><span style="font-size: 1rem;">Code challenges in every section — with full, downloadable solutions</span></li><li><span style="font-size: 1rem;">Builds from first principles — starting from basic Python/Numpy implementations and progressing to full PyTorch LLMs</span></li><li><span style="font-size: 1rem;">Suitable for researchers, engineers, and advanced learners who want to go beyond “black box” API usage</span></li><li><span style="font-size: 1rem;">Clear explanations without dumbing down the content — intensive but approachable</span></li></ul></div><div><br></div><div>Who Is This Course For?</div><div><ul><li><span style="font-size: 1rem;">Machine learning engineers and data scientists</span></li><li><span style="font-size: 1rem;">AI researchers and NLP specialists</span></li><li><span style="font-size: 1rem;">Software developers interested in deep learning and generative AI</span></li><li><span style="font-size: 1rem;">Graduate students or self-learners with intermediate Python skills and basic ML knowledge</span></li><li><span style="font-size: 1rem;">Technologies &amp; Tools Covered</span></li><li><span style="font-size: 1rem;">Python and PyTorch for deep learning</span></li><li><span style="font-size: 1rem;">NumPy and Matplotlib for numerical computing and visualization</span></li><li><span style="font-size: 1rem;">Google Colab for free GPU access</span></li><li><span style="font-size: 1rem;">Hugging Face Transformers for working with pre-trained models</span></li><li><span style="font-size: 1rem;">Tokenizers and text preprocessing tools</span></li><li><span style="font-size: 1rem;">Implement Transformers in PyTorch, fine-tune LLMs, decode with attention mechanisms, and probe model internals</span></li></ul></div><div><br></div><div>What if you have questions about the material?</div><div><br></div><div>This course has a Q&amp;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&amp;A by posting to ongoing discussions.</div><div><br></div><div><span style="font-size: 1rem;">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.</span></div><div><br></div><div>Enroll now and start mastering Large Language Models from the ground up.</div>

What you'll learn:

• Large language model (LLM) architectures, including GPT (OpenAI) and BERT
• Transformer blocks
• Attention algorithm
• Pytorch
• LLM pretraining
• Explainable AI
• Mechanistic interpretability
• Machine learning
• Deep learning
• Principal components analysis
• High-dimensional clustering
• Dimension reduction
• Advanced cosine similarity applications