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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 Learning
  • Unsupervised
  • Semi-Supervised
  • Reinforcement Learning

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  1. Topics

Supervised, Unsupervised, & Reinforcement Learning

PreviousStructured vs Unstructured DataNextTensorBoard

Last updated 5 years ago

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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.

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.

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.

There are numerous supervised learning algorithms and each has benefits and drawbacks. Read more about models.

See types of models.

types of supervised learning
Unsupervised Learning
Source: IBM