aakashns/deep-learning-workbook

A universal workflow for solving machine learning problems

Deep Learning Workbook

The Jupyter notebook deep-learning-workbook.ipynb outlines a universal blueprint that can be used to attack and solve any machine learning problem. It is based on the workflow described in the book Deep Learning with Python.

Usage Instructions

1. Set up your dev environment with Jupyter, Tensorflow & Keras (or any other ML framework). Follow this guide if you wish to use a GPU on AWS.

2. Download the latest version of the workbook using the command:

wget https://raw.githubusercontent.com/aakashns/deep-learning-workbook/master/deep-learning-workbook.ipynb

3. Change the file name, title and kernel as desired. This notebook was originally written with the kernel conda:tensorflow_p36 on the AWS Deep Learning AMI.

4. Follow the steps described in to notebook, filling in the blanks marked as TODO.

5. Once you're done building the final model, you can delete the cells containing instructions.

Deep Learning Workflow

See the Jupyter notebook deep-learning-workbook.ipynb for the detailed step-by-step workflow for solving machine learning problems using Deep Learning. Following is a short summary of the workflow:

1. Define the problem at hand and the data you will be training on; collect the data or annotate it with labels.

2. Choose how you will measure success on your problem. Which metrics will you be monitoring?

3. Determine your evaluation protocol: hold-out validation? K-fold validation? Which portion of the data should you use for validation?

4. Develop a first model that does better than a basic baseline: a model that has "statistical power".

5. Develop a model that overfits.

6. Regularize your model and tune its hyperparameters, based on performance on the validation data.

Credits

The Jupyter notebook is based on the universal workflow for machine learning outlined in the book Deep Learning With Python by François Chollet, the author of Keras.