HyperNEAT: Scaling Neuroevolution with Geometric Patterns
How HyperNEAT uses indirect encoding and geometric patterns to evolve large-scale neural networks with biological …
Machine-Learning
NEAT: Evolving Neural Network Topologies
Learn about NEAT's approach to evolving neural networks: automatic topology design, historical markings, and speciation …
Breaking Down Machine Learning for the Average Person
Discover how machine learning actually works through three fundamental approaches, explained with everyday examples you …
Fundamentals of Logic: Knowledge Representation
Learn about the foundations of AI logic: syntax, semantics, entailment, and inference algorithms for building …
An Introduction to Knowledge-Based Agents
Learn about knowledge-based agents: how AI systems use knowledge bases, reasoning, and inference to build intelligent …
QuAC: Question Answering in Context Dataset
Analysis of QuAC's conversational QA through student-teacher interactions, featuring 100K+ context-dependent questions …
CoQA Dataset: Advancing Conversational Question Answering
Analysis of CoQA, a conversational QA dataset with multi-turn dialogue, coreference resolution, and natural answers for …
GAN Objective Functions: A Comprehensive Guide
Complete guide to GAN objective functions including WGAN, LSGAN, Fisher GAN, and more. Understand which loss function to …
Understanding Generative Adversarial Networks (GANs)
Learn about GANs with intuitive explanations and mathematical foundations. Learn how adversarial networks generate …
Count Vectorization with scikit-learn in Python
Learn count vectorization in Python: convert text to numerical vectors using scikit-learn's CountVectorizer with …
Word Embeddings in NLP: An Introduction
Learn about word embeddings in NLP: from basic one-hot encoding to contextual models like ELMo. Guide with examples.