Intro

initial code unashamedly borrowed from: https://github.com/mathspp/NNFwP/tree/v1.1 see: https://mathspp.com/blog/neural-networks-fundamentals-with-python-mnist

Creating the Python environment (using Conda)

$ conda env create --name ml101 --file conda-env.yaml
$ conda activate ml101

Running

(ml101) $ python mnist.py

References

• Workflow diagram: https://www.linkedin.com/pulse/data-science-workflow-matt-dancho/
• Model tour: https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
• Model tour: https://towardsdatascience.com/a-tour-of-machine-learning-algorithms-466b8bf75c0a
• "NN Zoo" diagram: https://www.asimovinstitute.org/neural-network-zoo/
• Data splitting: https://glassboxmedicine.com/2019/09/15/best-use-of-train-val-test-splits-with-tips-for-medical-data/
• OpenAI Minecraft: https://openai.com/blog/vpt/
• OpenAI DALL-E 2: https://bdtechtalks.com/2022/04/11/openai-dall-e-2/
• OpenAI DALL-E 2: https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/