/ML-elective-resources

Resources for learning more for various ML concepts to supplement my ML elective lectures @ Hochschule Darmstadt (Summer Term 2019)

ML-elective-resources

Resources for learning more for various ML concepts to supplement my ML elective lectures @ Hochschule Darmstadt (Summer Term 2020)

Lecture & office hours slides

Preliminary ML Learning Material

TensorFlow setup & examples

Deliverables

Exposé: due Friday 3 July

Final Project: presentation during week of 17 July

Foundational ML Concepts

Overviews of ML Concepts

An Overview of Deep Learning for Curious People (by Lilian Weng from OpenAI)

3Blue1Brown Neural networks videos with excellent visualizations

Machine Learning for Everyone: Longform overview blogpost with nice graphics

Michael Nielsen's Neural Networks and Deep Learning E-Book

Types of Networks

RNNs

RNNs: Overview & Remembering What's Important

Understanding LSTM networks by Chris Olah

Unreasonable Effectiveness of RNNs

4 Stages of ML Process

1. Frame the ML Problem

Google Machine Learning Interactive Course: Problem Framing

Powered by TensorFlow YouTube Playlist Playlist of inspiring use cases of TensorFlow for idea generation for your projects

Machine Learning Yearning Free Short Book/Guide to Structuring ML Projects

2. Prepare Data

Datasets

TensorFlow Datasets

Open Datasets

Blog Post (Oct 2018): "The Best Public Datasets for Machine Learning and Data Science"

Preparation

Google Machine Learning Interactive Course: Data Preparation

3. Train ML Model

Example Projects & Tutorials

TensorFlow Official Tutorials TensorFlow JavaScript Demos

Seedbank: Collection of Interactive ML Examples (by Google Research)

Other TensorFlow Resources

TensorFlow Annual Dev Summit Videos (March 2019)

Debugging Training Issues

TensorFlow Tutorial: Get Started with TensorBoard

4. Predict Using ML Model