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4 years ago

Deep Learning Workbook

Source: https://github.com/aakashns/deep-learning-workbook

This Jupyter notebook 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

  1. 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.

  2. Follow the steps described below, filling in the blanks (marked as TODO).

  3. Once you're done building the final model, you can delete the cells containing instructions (like this one).

Step 1: Define the Problem & Collect Data

Define the problem and assemble a dataset:

  • What will your input data be? What are you trying to predict?
  • What type of problem are you facing?
    • Binary classification
    • Multi-class classification
    • Scalar regression
    • Vector regression
    • Multi-class, multi-label classification
    • Clustering
    • Generation
    • Reinforcement learning

Be aware of the hypotheses you are making at this stage:

  • You are hypothesizing that your outputs can be predicted given your inputs
  • You are hypothesizing that your available data is sufficiently informative to learn the relationship between inputs and outputs.

Remember that machine learning can only be used to memorize patterns which are present in your training data. You can only recognize what you have seen before.

Answer the following questions to define your problem:

Q: What are you trying to predict?

A: TODO

Q: What will your input data be?

A: TODO

Q: What type of problem are you facing?

A: TODO

Q: What is the size of your dataset?

A: TODO