Glossary

Methods

  • DNN: Dense (=fully connected) Neural Network
  • CNN: Convolutional Neural Network
  • RNN: Recurrent Neural Network
  • VAE: Variational Auto-Encoder
  • GAN: Generative Adversarial Network
  • LLM: Large Language Model
  • VLM: Vision Language Model
  • GNN: Graph Neural Network
  • PINNs: Physics-Informed Neural Networks

Datasets

Softwares

  • scikit-learn: a Python library for machine learning and data science (built on numpy and scipy)
  • numpy: a Python library for scientific computing (mainly linear algebra)
  • scipy: a Python library for scientific computing (collection of mathematical algorithms and convenience functions)
  • pandas: a Python library to manipulate tabular data/data frame
  • Keras: a user-friendly Python2 library for deep learning (wrapper around PyTorch, TensorFlow and JAX3)
  • Lightning: a Python library implementing high-level interface around PyTorch library to build and train neural network
  • PyTorch: a Python4 library for deep learning using GPUs and CPUs based on the Torch machine learning library
  • TensorFlow: a Python5 library for deep learning using GPUs and CPUs

References

Harrison, David, and Daniel L Rubinfeld. 1978. “Hedonic Housing Prices and the Demand for Clean Air.” Journal of Environmental Economics and Management 5 (1): 81–102. https://doi.org/10.1016/0095-0696(78)90006-2.
LeCun, Yann, and Corinna Cortes. 2010. “MNIST Handwritten Digit Database.” http://yann.lecun.com/exdb/mnist/.

Footnotes

  1. See here regarding ethical issues about this dataset↩︎

  2. and R, see {keras} package↩︎

  3. JAX: a high performance array computing Python library↩︎

  4. and R, see {torch}package↩︎

  5. and R, see {tensorflow}package↩︎