Search across the knowledge system.
One query can surface Academy lessons, model dossiers, projects, innovations, research and published articles.
Ranked from CortexLab’s local platform index and published content.
Contextual Bandit
Chooses among actions using context while learning from immediate reward.
Reinforcement Learning & Decision Systems
Learn MDPs, value methods, policy learning and safe decision optimization.
Bandits
Learn which action performs best while continuing controlled exploration.
Game-Based Learning Engine
A technical game platform where challenge selection responds to mastery, error patterns and pace rather than using empty engagement mechanics.
Machine Learning Core
Move from intuition to evaluated supervised and unsupervised learning systems.
Deep Learning Systems
Understand neural networks from optimization to deployment and monitoring.
Q-Learning
An off-policy temporal-difference method that learns action values from reward transitions.
Q-learning
Learn action values off-policy from temporal-difference targets.
TD learning
Build a working mental model of TD learning then connect it to implementation evaluation and production trade-offs.
Knowledge Graph Learning
Connects topics, prerequisites, projects, innovations, blog posts and interview concepts so Cortex can explain relationships and learning paths.
Hybrid Recommendation Engine
A recommendation stack combining content similarity, behavioral events, skill gaps and exploration so learning recommendations remain useful even during cold start.
Human-in-the-loop learning
Corrections become candidates with provenance and review rather than instant truth.
Cortex Local Intelligence Engine
The local NLP, retrieval, confidence, MDP-state and reinforcement-feedback engine powering Ask Cortex without an external generative-model API.
Multilayer Perceptron
A stack of learned affine transformations and nonlinearities that approximates complex functions.
Thompson Sampling
Samples action quality from posterior beliefs to balance exploration and exploitation.
Graph Neural Network
Learns node/edge representations by passing messages across graph neighborhoods.
Adaptive Interview Coach
A technical interview engine that samples from large domain banks, adapts difficulty, tracks weak concepts and schedules targeted review.
SQL
Query relational data accurately and reason about joins, grouping and performance.
Classification
Estimate class labels or probabilities and choose thresholds from real error costs.
Ensembles
Combine multiple weak or diverse learners to reduce error.
Embeddings
Represent items as dense vectors so semantic similarity can be measured.
SQL
Query relational data accurately and reason about joins, grouping and performance.
Autonomous SEO Lab
A guarded SEO research environment that proposes and tests improvements without allowing an optimizer to silently rewrite production content.
Adaptive Interview Engine
Practice technical interviews with adaptive question selection and weak-topic review.
Owned intelligence
Build inspectable local NLP, retrieval, routing and learning components before depending on remote black boxes.
Decision optimization
Use MDPs, bandits and Q-value policies where sequential or online choices genuinely exist.
Product intelligence
Combine learning, analytics, personalization and automation into a coherent user experience.
Logistic Regression
A probabilistic classification baseline that is transparent, fast and surprisingly competitive.
MLP
Build feed-forward neural networks and understand optimization behavior.
CNN
Learn local spatial patterns with shared convolutional filters.
Attention
Let a model dynamically weight which context elements matter for each representation.
Adaptive Difficulty Controller
Chooses challenge difficulty from recent correctness, response time, hint usage and mastery estimates to maintain productive difficulty.