1. Deep Teaching: The Sexiest Job of the Future and One Deep Learning Virtual Machine to Rule Them All
Microsoft Research has a recent paper (Machine Teaching: A New Paradigm for Building Machine Learning Systems) that explores the eventual evolution of Machine Learning. The paper makes a clear distinction between Machine Learning and Machine Teaching. The authors explain that Machine Learning is what is practiced in research organizations and Machine Teaching is what will eventually practiced by engineering organizations. The teaching perspective is not only different from the learning perspective, but there are obvious advantages in that concept disentanglement is known a priori: The paper concludes with three key developments that will be required by Machine Teaching to make progress: To truly meet this demand, we need to advance the discipline of machine teaching. This shift is identical to the shift in the programming field in the 1980s and 1990s. This parallel yields a wealth of benefits. This paper takes inspiration from three lessons from the history of programming.
- The first one is problem decomposition and modularity, which has allowed programming to scale with complexity.
- The second lesson is the standardization of programming languages: write once, run everywhere.
- The final lesson is the process discipline, which includes separation of concerns, and the building of standard tools and libraries.
2. PyTorch vs TensorFlow — spotting the difference
In this post I want to explore some of the key similarities and differences between two popular deep learning frameworks: PyTorch and TensorFlow. Why those two and not the others? There are many deep learning frameworks and many of them are viable tools, I chose those two just because I was interested in comparing them specifically.
- Difference #0 — adoption
- Difference #1 — dynamic vs static graph definition
- Difference #2 — Debugging
- Difference #3 — Visualization
- Difference #4 — Deployment
- Difference #5 — A Framework or a library
Conclusion TensorFlow is very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. It has production-ready deployment options and support for mobile platforms. TensorFlow is a good option if you:
- Develop models for production
- Develop models which need to be deployed on mobile platforms
- Want good community support and comprehensive documentation
- Want rich learning resources in various forms (TensorFlow has entire an MOOC)
- Want or need to use Tensorboard
- Need to use large-scale distributed model training
PyTorch is still a young framework which is getting momentum fast. You may find it a good fit if you:
- Do research or your production non-functional requirements are not very demanding
- Want better development and debugging experience
- Love all things Pythonic
3. The mostly complete chart of Neural Networks, explained
The zoo of neural network types grows exponentially. One needs a map to navigate between many emerging architectures and approaches. Fortunately, Fjodor van Veen from Asimov institute compiled a wonderful cheatsheet on NN topologies. If you are not new to Machine Learning, you should have seen it before:
In this story, I will go through every mentioned topology and try to explain how it works and where it is used. Ready? Let’s go
4. Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic.
5. deeplearn.js
deeplearn.js is an open-source library that brings machine learning building blocks to the web, allowing you to train neural networks in a browser or run pre-trained models in inference mode.
6. THE RISE OF AI IS FORCING GOOGLE AND MICROSOFT TO BECOME CHIPMAKERS