AI Leader: Generative AI & Agentic AI for Leaders & Founders

AI Leader: Generative AI & Agentic AI for Leaders & Founders - From AI Strategist to AI Leader: An Actionable Workshop for Leaders & Founders to Deliver Measurable ROI with LLMs

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his workshop equips business leaders to champion AI initiatives, then deliver and deploy AI solutions, driving change across the organization with measurable commercial impact.

This is quite different from a traditional “course”!

It’s actually not a course at all: it’s a briefing. A comprehensive, action-oriented briefing on Gen AI, designed by leaders, for leaders.

We will cover:

    AI expertise from a commercial perspective

    Real-world use cases; with success stories and failures

    Actionable toolkits to apply to your business

    From Stealth Startup to Global Enterprise

We will not cover:

    Technical detail; but sufficient to drive decision-making - so we do cover RAG, fine-tuning and Agents from a business point-of-view

    Using different AI tools yourself; this briefing is about transforming your organization to use AI tools!

If you are an executive, an entrepreneur, a leader, or if you are on a path to become one, then this briefing will position you for commercial success with Generative AI.

What you will learn

AI strategy, AI decision-making and AI leadership

This workshop builds expertise through 3 modules:

Module 1: Become an AI Strategist

    Your path to AI Leadership, driving change with measurable commercial impact

    The big picture: AI, Gen AI, LLMs, Agents, Reasoning

    The Models: Frontier, Open-Source, Cost / Performance / Security implications

    Commercial details: GPT wrappers vs Proprietary Models, expert prompting, hallucinations, alignment risks.

    Be an AI Strategist: a framework for your business to drive strategy and investment decisions

Module 2: Become an AI Decision maker

    The AI scaling laws and why they matter for your business

    5 core techniques for scaling AI performance with cost/benefit - from RAG to Reasoning

    Agents and Agentic AI - risks, benefits, opportunities

    AI Architecture from a commercial perspective - cross functional decision-making

    Be an AI Decision maker: a framework for your business to drive commercial decision-making

Module 3: Become an AI leader

    Gen AI as a competitive differentiator for your business - ideation and traps

    Skills, roles, org chart, governance and common talent challenges

    The case for R&D: committing to a Roadmap with uncertain outcomes

    Position yourself for change: opportunities & risks of Agents and workforce impact

    Be an AI Leader: a framework for leading AI initiatives with measurable commercial impact

A deep understanding of AI large language model mechanisms

A deep understanding of AI large language model mechanisms - Build and train LLM NLP transformers and attention mechanisms (PyTorch). Explore with mechanistic interpretability tools

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Description

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

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.

Who this course is for:

•     AI engineers
•     Scientists interested in modern autoregressive modeling
•     Natural language processing enthusiasts
•     Students in a machine-learning or data science course
•     Graduate students or self-learners
•     Undergraduates interested in large language models
•     Machine-learning or data science practitioners
•     Researchers in explainable AI