General steps includes:
- Load in the data
- Apply a ML algorithm on the data
- Look at the accuracy (How good is the classifier?)
Notes:
- Between step1 and step2 we want to preprocess the data
Generally there are 3 steps to compute output of the perceptron:
- Dot product
- Add a bias
- Take non-linearity
- https://jupyter-notebook.readthedocs.io
- https://www.tensorflow.org/
- https://scikit-learn.org/
- https://pytorch.org/
- https://github.com/pyro-ppl/pyro
- https://github.com/facebookresearch/ParlAI
- https://github.com/PAIR-code/facets
- https://github.com/facebookresearch/Detectron
- https://www.introtodeeplearning.com
- google.ai/pair
- https://ml-showcase.com/
- https://github.com/collections/machine-learning
- https://github.com/josephmisiti/awesome-machine-learning
- https://github.com/EthicalML/awesome-production-machine-learning
- https://github.com/onmyway133/fantastic-machine-learning
- Facets
- what-if-tool
- Embedding Projector