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DATA-X:
m420 - BREAD & BUTTER DEEP LEARNING - REGRESSION AND CLASSIFICATION USING TENSORFLOW v2 AND LUDWIG



Author List (in no particular order): Elias Castro Hernandez, Ikhlaq Sidhu, and Debbie Yuen.

About (TL/DR): Tensorflow (TF) is an open-source library used for dataflow, differentiable programming, symbolic math, and machine learning applications such as deep learning neural networks. Ludwig is a toolbox built atop TF that enables users the ability to train and test deep learning models without the need to write code.

Learning Goal(s): This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy deep learning regression and classification models, using structured data. This task is so common to machine learning, that it is pretty much the bread and butter of ML engineers.

Associated Materials: To ease the learning curve, we encourage the user of this notebook to visit the Data-X site for resources on fundamentals of machine learning, and an introductoin to TensorFlow v2.

Keywords (Tags): tensorflow, tensor-flow, tensorflow-tutorial, deep-learning, deep-learning-with-python, neural-networks, Ludwig, deep-learning-toolbox, data-x, uc-berkeley-engineering

Prerequisite Knowledge: (1) Python, (2) Pandas, (3) Matplotlib, (4) TensorFlow v2

Target User: Data scientists, applied machine learning engineers, and developers

Copyright: Content curation has been used to expedite the creation of the following learning materials. Credit and copyright belong to the content creators used in facilitating this content. Please support the creators of the resources used by frequenting their sites, and social media.

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CONTENT

  • m420_dl_bread_and_butter_regression_classification_TF_LUDWIG -- Regression and classification with TFv2 and Ludwig.
  • assets/homeworks/ -- Contains several exercises to help you master the material.

I. LINEAR REGRESSION

Simple Architecture

1) PART 3.1: PROBLEM DEFINITION AND SETUP
2) PART 3.2: MODEL
3) PART 3.3: GENERALIZATION AND PREDICTIONS

II. IMAGE CLASSIFICATION

Simple Architecture

1) PART 2.1: TO DO

III. WRAP UP AND NEXT STEPS

You've completed the introduction to TensorFlow V.2, and one can assume that you are ready to get things done with your new knowledge. Visit the Data-X website to learn how to use TensorFlow to tackle various deep learning problems, or use the following links to some topics of interest:

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# reg_clas_TF_LUDWIG