Duration: 1 day
There are many mishaps in perceptions people have about AI. This course will show you how AI really works. You can then incorporate it in your business strategy and day-to-day tasks. It includes the principles of AI. Next, a quick tour of LLMs and Neural Networks. Then a demonstration of how Python AI libraries can be used to classify fashion images.
Content
1.AI Concepts
2.Large Language Models Unveiled
3.Neural Networks Explored
4.AI in Action
Who Will Attend
This workshop is designed for professionals and creators who want to truly understand how modern AI works beneath the surface. It’s ideal for tech‑curious attendees and early‑career developers who use AI tools but want to grasp the concepts behind AI. Business leaders, product teams, and digital specialists will gain a clear, jargon‑free understanding of supervised learning, neural networks, and large language models.
Five Benefits of Attending the Talk
- Understand AI from first principles — Attendees gain a grounded explanation of what AI actually is, including supervised vs. unsupervised learning, classification, clustering, and real‑world applications.
- Demystify Large Language Models (LLMs) — The session breaks down how LLMs work, including transformers, self‑attention, training, and fine‑tuning, helping people understand systems like ChatGPT and Copilot.
- See neural networks explained simply — Participants learn how neural networks learn patterns, adjust weights, and build abstractions, making complex concepts accessible.
- Watch AI in action with real code — The Keras walkthrough shows how to build, train, and use a neural network for image classification, giving attendees practical insight into how models are built.
- Leave with resources and demos — Downloadable workshop notes and code examples allow attendees to continue learning after the session.
Course Outline:
Module 1 — AI Concepts & Foundations
Overview:
Ground participants in the core ideas behind modern AI, using your introductory slides on definitions, supervised/unsupervised learning, classification, and clustering.
Key Topics:
- What AI is and is not
“Artificial Intelligence (AI) is the capability of computer systems to perform tasks that normally require human intelligence…” - AI jargon explained simply
- Supervised learning
- Training data
- Classification vs regression
- Unsupervised learning
- Clustering
- Dimensionality reduction
- Real‑world applications
(Healthcare, fraud detection, customer service, autonomous vehicles, cybersecurity)
Activities:
Interactive clustering exercise using 2‑D data (mirroring your slides).
Module 2 — Large Language Models Unveiled
Overview:
Deep dive into LLMs using your sections Unveiling Large Language Models, Understanding LLM Functionality, and ChatGPT and LLM Integration.
Key Topics:
- What LLMs are
“Large Language Models are AI systems trained on vast text data…” - How LLMs learn language patterns
- Next‑word prediction and neural network foundations
- Fine‑tuning and domain‑specific adaptation
- How ChatGPT uses LLMs for natural language understanding, contextual suggestions, and productivity support
Activities:
Prompt‑engineering mini‑lab: rewriting prompts to observe model behaviour.
Module 3 — Neural Networks Explored
Overview:
Build conceptual understanding of neural networks, using your slides on neurons, layers, abstraction, and backpropagation.
Key Topics:
- Neural networks inspired by the brain
“Interconnected ‘neurons’ that process information in layers.” - Feature extraction: edges → shapes → concepts
- Backpropagation and weight updates
- How neural networks power LLMs
- Transformer architecture
- Self‑attention
- Positional encoding
- Layer normalization
Activities:
Walkthrough of a simple neural network diagram and attention visualisation.
Module 4 — AI in Action: Hands‑On with Keras
Overview:
A practical module based on your Keras walkthrough: loading data, building a model, training, and making predictions.
Key Topics:
- Overview of modelling algorithms (Regression, Naïve Bayes, K‑Means, Neural Networks)
- Keras estimator API
“Consistent interface API… easily configurable via hyperparameters.” - Step‑by‑step fashion‑MNIST classification example:
- Importing TensorFlow/Keras
- Loading datasets
- Visualising images
- Labelling items
- Building model layers
- Training the model
- Making predictions
“Three images are classified 100% correctly. Wow!!”
Activities:
Create a simple neural network diagram and attention visualisation.
Course booking
- Complete the form below or email david@talk-it.biz
- Where Rengen House
- 4 Argyle Street, Bathwick, Bath BA2 4BA.
- When: 9th June 2026
- Cost: £150
- Start Time 9.30 AM
- End Time 5.00m PM