AI and Machine Learning Topics
Artificial Intelligence - is a goal
Reinforcement Learning
Speech Recognition
Emergent Behavior
Augmented Programming
Algorithm Building
AI Ethics
Machine Learning - is a strategy
Supervised Learning
Unsupervised Learning
Hypothesis Testing
K-means Clustering
K-Nearest Neighbors
Logistic Regression
Linear Regression
Principal Component Analysis
Support Vector Machines
Decision Trees
Neural Networks - is a tool
Perceptron
Backpropagation
Feed Forward Networks
Hopfield Networks
Boltzmann Machines
Liquid State Machines
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Transformers
Autoencoders
Long Short-Term Memory Networks
Radial Basis Function Networks
Self-Organizing Maps
Deep Learning - optimization of neural networks
Transformers
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Generative Adversarial Networks (GAN)
Long Short-Term Memory Networks (LSTM)
Autoencoders
Deep Belief Networks
Deep Reinforcement Learning
Epochs and Batches
General AI - highly specialized outcome of scale and data
Large Language Models (LLM)
Foundation Models
Generative Adversarial Networks (GAN)
BigGAN
Transfer Learning
N-Shot Learning
Zero-Shot Learning (ZSL)
One-Shot Learning (OSL)
Reinforcement Learning with Human Feedback (RLHF)
LoRA (Low-Rank Adaptation)
Ensemble Models
Agents
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