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I'm currently working on repos for an upcoming Manning book on machine learning (gradient boosting and deep learning) with tabular data.

I've created "hello world" examples that work in Colab for the follwoing deep learning with tabular data stacks:

And I'd be interested to get "hello world" examples working for the following deep learning with tabular data stacks:

  • TabNet on TensorFlow
  • DeepTables
  • SAINT
  • PyTorch Tabular

Mark Ryan's Projects

ai_conference_june_2019 icon ai_conference_june_2019

Material for "Using deep learning and time-series forecasting to reduce transit delays" at O'Reilly AI Conference in Beijing, June 2019

code_interpreter icon code_interpreter

Example of runnable output from Code Interpreter for a prompt asking for a Keras model for a simple CSV dataset.

course20 icon course20

Deep Learning for Coders, 2020, the website

deep_learning_basics icon deep_learning_basics

repo for deep learning intro for upcoming Manning book on machine learning with tabular data

deep_learning_for_structured_data icon deep_learning_for_structured_data

Production repo to accompany Deep Learning with Structured Data book from Manning: https://www.manning.com/books/deep-learning-with-structured-data

dl_auto_catalog icon dl_auto_catalog

explore deep learning models generated automatically from relational database catalog info

dl_structured_data_hands_on icon dl_structured_data_hands_on

Simplified repo for hands-on exercises related to Manning book Deep Learning with Structured Data https://www.manning.com/books/deep-learning-with-structured-data

fastai_vs_keras icon fastai_vs_keras

MNIST implemented in fastai and Keras as part of a comparison between the two high-level deep learning frameworks

fastbook icon fastbook

The fastai book, published as Jupyter Notebooks

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