This Review gives an overview of intersting stuff I stumbled over which are related to machine learning.
Live Demos, Websites and Blogs
- Anish Athalye: A Step-by-Step Guide to Synthesizing Adversarial Examples
- Aran Lunzer and Amelia McNamara: What's so hard about histograms?
- Martin Thoma: Document Classification
- Metadata: A Comparison of Distributed Machine Learning Platforms (Spark, PMLS, TensorFlow)
- Hu, Peiyun and Ramanan, Deva: Finding Tiny Faces (MATLAB)
- Mozilla: Common Voice
- Slav Ivanov: 37 Reasons why your Neural Network is not working
- Humble Book Bundle - Data Science
- Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI
- DeepL: Neues Tool übersetzt viel besser als Google und Microsoft
- TensorFlow Debugger Screencast
- Martin Thoma: Analysis and Optimization of Convolutional Neural Network Architectures
- Abhinav Gupta (Google): Revisiting the Unreasonable Effectiveness of Data
- Why are Machine Learning models called black boxes?
- What is an appropriate way to compare classifiers with different sets of classes?