Learning.
Where I'm Headed
ENGINEERING & APPLIED AI ROADMAP
LEARNING PATHWAY.
PHASE 01 //
Foundations & Baseline
PAST · MASTEREDPHASE 02 //
Active Implementation
CURRENT FOCUS (NOW)PHASE 03 //
Deep Architecture
UPCOMING DRILL (NEXT)PHASE 04 //
Frontier Systems
ADVANCED HORIZON (FUTURE)Machine Learning Core
Statistical learning theory, algorithms & validation
Neural Networks & Architectures
Loss gradients, multi-layer networks & backprop
Computer Vision (CV)
Pixel matrices, spatial convolutions & visual representation
Natural Language Processing (NLP)
Tokenization, semantic vector embeddings & sequence models
MILESTONE INSPECTOR
Machine Learning CoreCURRENT STUDY DRILL (NOW)
Active ImplementationSupervised Learning
Currently active daily drill: Training classification & regression algorithms, cost function minimization, cross-validation, and hyperparameter tuning.
TECHNICAL MODULES & COMPETENCIES
Linear & Logistic Regression
Decision Trees & Ensemble Forests
K-Fold Cross-Validation & Grid Search
Precision, Recall, ROC-AUC Evaluation
ASSOCIATED LIBRARIES & STACK
PythonScikit-learnPandasMatplotlib
ULTIMATE CAREER HORIZON · CULMINATION
VISION + NLP FUSION
ULTIMATE GOAL: MULTIMODAL AI SYSTEMS
Unified Vision-Language Intelligence (CV + NLP + Deep Architectures)
The convergence of Computer Vision and Natural Language Processing into unified latent embeddings — enabling cross-modal contrastive learning (CLIP), Vision-Language Models (VLM), visual question answering, and embodied AI systems.
TARGET MULTIMODAL COMPETENCIES
Cross-Modal Contrastive Learning (Image-Text Alignment)
Vision-Language Model (VLM) Architectures (e.g. CLIP, LLaVA)
Joint Latent Representation & Zero-Shot Classification
Spatial-Temporal Multimodal Reasoning
Computer Vision (CV)Spatial feature maps & ViT
NLP & SemanticsContextual self-attention & LLMs
Multimodal FusionJoint latent space alignment