Skip to content
An Absolute Beginners Guide to Machine Learning with PyTorch
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
images
.amlignore
.gitignore
LICENSE
README.md
cloud.ipynb
requirements.txt
score.py
start.ipynb
train.py

README.md

TL;DR

Azure Notebooks

Do this:

  1. Click the Launch button above. In the new page that opens click the "Import" button to import the notebook.
  2. Run start.ipynb to learn about ML and PyTorch
  3. Run cloud.ipynb to learn about AzureML service
  4. (other things)
  5. Become a multi-millionaire AI startup founder

Add an issue to the repo if something doesn't work so I can fix it!

Machine Learning Quickstart

The purpose of this brief tutorial is to get you oriented on the Machine Learning Process. Machine Learning (ML) is basically a different way of creating "code" or something that is executed much in the same way code is executed. The primary difference is that machine learning has set classes of execution paths (usually called models) that have fixed parameters that need to be "learned." For supervised learning these parameters are optimized by giving ML algorithms examples of the inputs and answers. The process usually proceeds as follows:

  1. Start with a question
  2. Find relevant data
  3. Select appropriate items from the data
  4. Choose a model type
  5. Optimize the model parameters
  6. Save model
  7. Put model into production

The notebooks presented in this repo are divided into two overlpping sections:

  • Problem, Data, Experimentation - Local
  • Large Scale Experimentation, Deployment - Cloud

Local

The starting notebook shows the local approach to the Machine Learning Process. In this notebook you will find the first 5 steps outlined above but on a smaller scale (in this case the problem is small as is - generally for larger problems we begin on a small scale and then once some of the hypothesis are proved we move to large scale).

Cloud

The cloud notebook shows what a large scale approach would be using Azure Machine Learning service and covers steps 4-7 outlined above. In this case the dataset size has not changed at all and can be run locally without problem but you can imagine a scenario where we test out our thoughts on a small subset of all available data locally and then move to cloud scale when testing our hypothesis' on the full dataset.

Problem

Because I am more interested that you learn the process I decided to go with the "Hello World" of Machine Learning: predicting digits.

Digit Examples

Basically, given a 28x28 pixel grayscale image, can you predict the actual handwritten number? Mathematically speaking, given a 784 sized vector of numbers between 0-255, can you return the corresponding digit?

Questions and/or Suggestions

If there is any part of this that is hard to understand please add an issue to the repo! Would love to make this super easy to understand. My personal opinion is that everyone can understand the rudiments of what is happening!

You can’t perform that action at this time.