Fundamentals improve decision quality
Understanding model behavior helps you design better prompts, products, and evaluation criteria.
BUNKROS AI Training
Understand how modern AI systems learn, generalize, and fail.
A practical technical foundation in neural network mechanics, architectures, optimization, and model limitations.
Why This Matters
Strategic relevance before tactical execution.
Black-box thinking creates blind spots
Teams need conceptual clarity to detect failure modes and avoid overconfidence.
Architecture knowledge is strategic
Model choice, context windows, and scaling behavior directly impact product outcomes.
What You Will Learn
Practical capabilities you can apply immediately.
- Explain core neural network mechanics in plain technical language.
- Understand backpropagation, optimization, and training dynamics.
- Compare transformer, CNN, and diffusion architecture families.
- Identify overfitting, drift, and hallucination risk factors.
- Interpret benchmark claims with technical skepticism.
- Translate ML concepts into practical product decisions.
Curriculum Modules
A structured path from foundations to implementation.
Module 1: Neural Network Essentials
From perceptrons to deep networks and representation learning.
Module 2: Optimization and Training Dynamics
Loss functions, gradient descent, and convergence tradeoffs.
Module 3: Transformer Architecture
Attention mechanisms and sequence modeling fundamentals.
Module 4: Multimodal and Generative Architectures
How image, text, and video generation pipelines differ.
Module 5: Failure Modes and Limits
Bias, hallucination, brittleness, and context constraints.
Module 6: Applied Interpretation
Use technical understanding to guide product and policy choices.
30-Minute Training
One focused sprint to move from theory to repeatable execution.
00:00 - 05:00
Introduction
Define the problem this track solves, pick one real workflow, and set a measurable target for the session.
05:00 - 11:00
Theory Block 1
Map the core principles so your decisions are based on system behavior, not trial-and-error prompting.
11:00 - 17:00
Exercise Block 1
Run a controlled build task with explicit constraints, then measure output quality against your rubric.
17:00 - 23:00
Theory Block 2
Add governance, validation, and failure modes so the workflow remains usable in production.
23:00 - 30:00
Exercise Block 2 + Check
Refine your first build, run a quick knowledge check, and prepare your next learning sprint.
Theory Blocks
Foundations that keep your outputs reliable.
Fundamentals improve decision quality
Understanding model behavior helps you design better prompts, products, and evaluation criteria.
Black-box thinking creates blind spots
Teams need conceptual clarity to detect failure modes and avoid overconfidence.
Architecture knowledge is strategic
Model choice, context windows, and scaling behavior directly impact product outcomes.
Hands-On Exercises
Short builds designed for immediate skill transfer.
Exercise 1: Module 1: Neural Network Essentials
From perceptrons to deep networks and representation learning.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 2: Module 2: Optimization and Training Dynamics
Loss functions, gradient descent, and convergence tradeoffs.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Exercise 3: Module 3: Transformer Architecture
Attention mechanisms and sequence modeling fundamentals.
Build a focused workflow step in 6 minutes. Force explicit inputs, expected outputs, and review criteria.
Deliverable: one reusable prompt or SOP with acceptance criteria and one risk note.
Knowledge Check
Validate comprehension before scaling the workflow.
Open Resources
Continue learning with high-quality public material.
Glossary
Key terms you should be fluent in for this track.
Loss Function
Training signal that quantifies model error for optimization.
Overfitting
When a model memorizes training data and generalizes poorly to new inputs.
Tools Covered
Tooling choices tied to workflow outcomes.
PyTorch
TensorFlow
Jupyter
Weights & Biases
Hugging Face
NumPy
Colab
Who This Is For
Built for operators, builders, and strategic teams.
- Product and technical professionals needing deeper AI literacy.
- Engineers transitioning into ML-enabled product work.
- Analysts and consultants advising AI strategy.
- Founders who need practical model understanding for decisions.
Outcomes and Career Impact
Execution outcomes with direct professional value.
Outcome
Gain technical fluency for architecture and model discussions.
Outcome
Improve model selection and evaluation decisions.
Outcome
Communicate AI limitations credibly to stakeholders.
Outcome
Build stronger cross-functional collaboration with ML teams.
Signals from Practice
Operator-level feedback and implementation sentiment.
"It made complex ML concepts practical for our product team."
"Now we can challenge model assumptions with confidence."
Access Models
Free, cohort, and enterprise pathways.
Starter
EUR 0
Neural network fundamentals map and glossary.
Pro Cohort
EUR 529
6-week foundation with guided technical labs.
Enterprise
Custom
AI literacy upskilling for cross-functional teams.
Ready to Start
Build technical AI literacy that improves product decisions.
Master the foundations behind modern AI behavior and limitations.
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