CS 4973 & 6463 : Deep Learning

Syllabus | Schedule | Resources

TensorFlow Supplemental

There are many (many) machine learning frameworks available and their capabilities are expanding quickly.
In this course, we will use Google's free and open-source framework: TensorFlow. As the focus of the course is on deep learning concepts, this page is provided as supplementary material. If you have trouble implementing a concept, start here for a range of introductory to advanced assistance.

Tutorials & Resources

New to TensorFlow? Just rusty? Check out some of the following tutorials to find the right fit for you:

TensorFlow Tutorials - start here for basic information, FAQs, and introductory material
LearningTensorFlow.com - basics, linear algebra, machine learning
TensorFlow for Absolute Beginners - blog to build your 1st neural network
TensorFlow Tutorial for Beginners - build NN, train, evaluate, optimize

No time for that? Here are some cheatsheets to save time, if you've already completed Hello, World!

A TensorFlow Glossary/Cheat Sheet
TensorFlow examples Jupyter notebook

Books/reports available, for general reference:

Hands-On Machine Learning with Scikit-Learn and TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems, by Aurélien Géron
Machine Learning with TensorFlow, by Nishant Shukla - core machine learning with TensorFlow
Deep Learning with TensorFlow: Explore neural networks with Python, by Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms, by Sam Abrahams, Danijar Hafner, Erik Erwitt, Ariel Scarpinelli

Google Colaboratory

Google Colaboratory and TensorFlow were made for each other! Installing specific versions and libraries is simple, once you become familiar with their terminology.

Begin by creating your first colab notebook on colab.research.google.com, and review the "Hello, World" tutorial there. Here are a few extra resources (short blogs & colab notebooks) to get you started: