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Artificial Intelligence Wiki
Topics
Accuracy and Loss
Activation Function
AI Chips for Training and Inference
Artifacts
Artificial General Intelligence (AGI)
AUC (Area under the ROC Curve)
Automated Machine Learning (AutoML)
CI/CD for Machine Learning
Comparison of ML Frameworks
Confusion Matrix
Containers
Convergence
Convolutional Neural Network (CNN)
Datasets and Machine Learning
Data Science vs Machine Learning vs Deep Learning
Distributed Training (TensorFlow, MPI, & Horovod)
Generative Adversarial Network (GAN)
Epochs, Batch Size, & Iterations
ETL
Features, Feature Engineering, & Feature Stores
Gradient Boosting
Gradient Descent
Hyperparameter Optimization
Interpretability
Jupyter Notebooks
Kubernetes
Linear Regression
Logistic Regression
Long Short-Term Memory (LSTM)
Machine Learning Operations (MLOps)
Managing Machine Learning Models
ML Showcase
Metrics in Machine Learning
Machine Learning Models Explained
Model Deployment (Inference)
Model Drift & Decay
Model Training
MNIST
Overfitting vs Underfitting
Random Forest
Recurrent Neural Network (RNN)
Reproducibility in Machine Learning
REST and gRPC
Serverless ML: FaaS and Lambda
Synthetic Data
Structured vs Unstructured Data
Supervised, Unsupervised, & Reinforcement Learning
TensorBoard
Tensor Processing Unit (TPU)
Transfer Learning
Weights and Biases
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Supervised, Unsupervised, & Reinforcement Learning
Source: IBM

Supervised Learning

In supervised learning, the machine uses labeled training data. It is told the correct output and it compares its own output which informs the subsequent steps, adjusting itself along the way.
This process is repeated until the model achieves a desired level of accuracy on the training data and can correctly predict the class label for new instances.
There are numerous supervised learning algorithms and each has benefits and drawbacks. Read more about types of supervised learning models.

Unsupervised

In unsupervised learning, the data isn't labeled. The machine must figure out the correct answer without being told and must therefore discover unknown patterns in the data. Algorithms must therefore be formulated such that they can find suitable patterns and structures in the data on their own.
Some popular examples of unsupervised learning include GANs and Autoencoders.
See types of Unsupervised Learning models.

Semi-Supervised

In Semi-Supervised Learning: Input data is a mixture of labeled and unlabeled examples.

Reinforcement Learning

Reinforcement Learning allows the machine the most freedom. It uses trial and error to discover the actions that yield the greatest rewards. AlphaGo is a famous example of RL.
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Outline
Supervised Learning
Unsupervised
Semi-Supervised
Reinforcement Learning