Skip to content
brianletort.aiD. Brian Letort, Ph.D.

Lab · Learning

Learning resources with systems bias.

Courses, teaching, and interactive foundations for readers who want to understand the systems beneath the strategy.

Teaching philosophy

I do not just teach algorithms. I teach people how to think in systems: intuition first, real-world focus, no math gatekeeping, and production-ready skepticism about hype.

Pluralsight courses

17 public Pluralsight courses covering AI, RAG, LLM agents, data engineering, and enterprise AI. The list below is a featured subset of 11 verified titles; the full catalog lives on the Pluralsight author page.

Featured 11 of 17 · Full catalog: View Pluralsight author page

Aug 2025 · 59m · OpenAI / Testing / Intermediate

Testing, Monitoring, and Evaluating OpenAI Models

Production testing and evaluation strategies for OpenAI deployments.

Aug 2025 · 52m · React / AI / Beginner

AI-powered React Applications: Creating Data Visualizations

Building intelligent data visualizations with AI and React.

Jun 2025 · 42m · Agents / LLMs / Intermediate

Evaluating and Optimizing LLM Agents

Performance optimization and evaluation techniques for AI agents.

May 2025 · 1h 3m · Data Engineering / ML / Beginner

Data Engineering for Machine Learning

Building robust data pipelines that feed ML systems.

Apr 2025 · 29m · R / ML / Advanced

Model Validation and Hyperparameter Tuning in R

Advanced model validation and optimization techniques in R.

Mar 2025 · 27m · Agents / LLMs / Intermediate

Domain-specific LLM Agents

Building specialized AI agents for specific industry domains.

Nov 2024 · 18m · RAG / Strategy / Beginner

Expansion Strategies for RAGs

Scaling and expanding retrieval-augmented generation systems.

Sep 2024 · 18m · RAG / Multilingual / Beginner

Implementing Multilingual Generative AI Cross-lingual RAGs

Building RAG systems that work across multiple languages.

Aug 2024 · 22m · RAG / Knowledge Bases / Beginner

Integrating Knowledge Bases for RAGs

Connecting enterprise knowledge bases to RAG pipelines.

Jul 2024 · 4h 21m · Metaverse / Development / Intermediate

Metaverse: Development Platforms and Tools

Comprehensive guide to metaverse development ecosystems.

— · — · Node.js / Microservices / Intermediate

Node.js Microservices: API Gateway and Edge Services

Building scalable microservices with Node.js API gateways.

Interactive foundations

Interactive foundations for understanding how modern AI systems learn, represent information, and make predictions.

Foundation

Neural Network Forward Pass

Watch how data flows through layers, with weights and activations visualized.

Neural Network Forward Pass

Watch information flow through layers of learned transformations

InputHidden 1Hidden 2Hidden 3Output

The Mathematics

z[l]=W[l]a[l−1]+b[l]\mathbf{z}^{[l]} = \mathbf{W}^{[l]} \mathbf{a}^{[l-1]} + \mathbf{b}^{[l]}

Foundation

Gradient Descent Optimization

See how models learn by descending the loss landscape to find optimal parameters.

Gradient Descent: The Path to Optimization

Watch as the algorithm finds its way downhill through the loss landscape

Optimum

Step

0/-1

Loss

—

Learning Rate

0.1

θt+1=θt−α∇θL(θt)\theta_{t+1} = \theta_t - \alpha \nabla_{\theta} \mathcal{L}(\theta_t)

"Progress is made one step at a time, always moving toward lower loss."

Foundation

Transformer Attention Mechanism

Visualize how attention weights determine which tokens influence each other.

Transformer Attention: How AI Reads

See which words the model focuses on when processing each token

The
future
of
AI
is
human
augmentation
The
40
10
5
15
5
15
10
future
10
30
10
20
10
10
10
of
10
10
30
10
20
10
10
AI
15
20
10
35
5
10
5
is
5
10
15
10
30
20
10
human
10
15
5
15
15
25
15
augmentation
10
10
5
10
10
20
35
Attention(Q,K,V)=softmax(QKTdk)V\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V

"Understanding emerges not from seeing everything, but from knowing where to look."

Low attention
High attention

Foundation

Linear Algebra Transformations

The mathematical foundation of neural networks: matrix operations visualized.

Linear Transformations

How matrices reshape our view of the world

Original

Transformed: Identity

[1001]\begin{bmatrix} 1 & 0 \\ 0 & 1 \end{bmatrix}

"The identity matrix: everything stays exactly where it is."

Books

2019

The Machine Learning Toolbox: For Non-Mathematicians

Practical machine learning fundamentals without the PhD in statistics: mental models, intuitive explanations, and real-world examples.

Academic teaching

Teaching

Colorado Technical University

Adjunct faculty teaching master's and doctorate-level courses in AI, Data Science, Machine Learning, and Database Theory since 2010.

Teaching

Doctoral Mentorship

Mentoring doctoral candidates through dissertation research in AI, data systems, and enterprise architecture.