AI &amp; Machine Learning Engineering

Instance-Based Learning & k-Nearest Neighbors

7 min

Probabilistic Modeling & Naive Bayes

Support Vector Machines & The Kernel Trick

Decision Trees & CART Algorithm

Ensemble Theory: Bagging, Random Forests & Boosting

Neural Networks & Backpropagation Calculus

10 min

Model Evaluation, ROC Curves & Calibration

k-Means Clustering & Lloyd's Optimization

Unsupervised Machine Learning & Dimensionality Reduction

Mathematical derivations and zero-dependency Python and Rust implementations of k-Means, DBSCAN, Gaussian Mixture Models, PCA, t-SNE, and Isolation Forests.

Hierarchical Linkage & Density-Based DBSCAN

Gaussian Mixture Models & EM Algorithm

Principal Component Analysis & SVD

Manifold Learning: t-SNE & UMAP Mathematics

Anomaly Detection: Isolation Forests & LOF

Unsupervised Validation & Evaluation Metrics

Weight Initialization & Layer Normalization

Deep Learning, CNNs & Transformer Architectures

Mathematical derivations and zero-dependency Python and Rust implementations of Xavier/He initialization, ResNet skip connections, LSTM gates, Scaled Dot-Product Attention, and LoRA.

Convolutional Networks & ResNet Skip Connections

Recurrent Modeling: BPTT, LSTM Gates & GRU

Attention Mechanisms & The Transformer

Parameter-Efficient Fine-Tuning: LoRA Mathematics

Variational Autoencoders & ELBO Derivation

Generative AI, Large Language Models & Diffusion

Mathematical derivations and zero-dependency Python and Rust implementations of Variational Autoencoders, DDPM Diffusion, Decoder-Only Transformers, KV Cache, BPE, and Direct Preference Optimization (DPO).

Diffusion Models & DDPM Mathematics

Decoder-Only Transformers & KV Cache

Tokenization Engineering & BPE Algorithm

Preference Alignment: RLHF & DPO Mathematics

MDPs, Bellman Equations & Value Iteration

Reinforcement Learning & Autonomous Agents

Mathematical derivations and zero-dependency Python and Rust implementations of Bellman equations, Q-Learning, Double DQN, Policy Gradients, PPO, and the ReAct Autonomous Agent loop.

Temporal Difference Learning, SARSA & Q-Learning

10 min

Deep Q-Networks (DQN), Double DQN & Dueling Architectures

11 min

Policy Gradients, REINFORCE & Proximal Policy Optimization (PPO)

Autonomous Agents: ReAct, Memory & Tool-Calling Loops

Multimodal Alignment & Contrastive Learning (CLIP)

Multimodal AI, Graph Neural Networks & Production MLOps

Unify vision, language, and audio representations, master message passing on non-Euclidean graphs, and architect high-throughput LLM serving and model quantization engines.

11 min

Graph Neural Networks & Spectral/Spatial Message Passing

Production Serving Architectures & PagedAttention (vLLM)

Model Quantization: PTQ, QAT, AWQ & GPTQ