Part 1: The Machine Learning-Learning Landscape
For context, one of the many living + breathing resources focused on being able to break through the noise.
From where we stand in the fall of 2017, I would largely break out the knowledge transfer around deep learning (arguably the fastest growing and most exciting field of ML) into two distinct flows.
- Research Labs → Non-ML software engineering community
How to use deep learning.
2. Industry → Business and Product Community
How to learn and think critically about the potential impacts of deep learning.
Historically, a significant amount of the progress with developing and optimizing neural networks as well as releasing new large scale (or novel) datasets originates from within research labs.
These research labs are found both within top tier universities (CMU, NYU, Berkeley, Stanford, MIT, U of Toronto, UIUC, U of Montreal, etc) and large technology companies (Google Research, Google Deepmind, Microsoft, IBM, Baidu, Facebook, etc). For more context on the current tension and brief history go here and here.
Below are a list of resources (courses, tutorials, podcasts, newsletters, videos, etc) that directly address part 1. These have caught my eye assisting with non-machine learning experts get their start understanding, experimenting, and applying what can be very complex mathematical and engineering concepts.
Research Labs → Non-ML software engineering community
Distill 📖
A new online journal dedicated to clear explanations of machine learning.
Fast.ai ⌨️
Popular online courses aimed to get students up and running with deep learning.
Rachel and I both share real disappointment as to how accessible deep learning is at the moment…a lot of the barriers of getting into the field are unnecessary. — Jeremy in conversation with ArchiTECHt.
Kaggle 📊
An online platform for data-mining and predictive-modeling competitions.
MOOCS 💻
- Coursera: Machine Learning — Andrew Ng
- Coursera: Neural Networks for Machine Learning-Geoff Hinton
- Additional recommended MOOC alternatives, here, here, and here.
Import.ai 📓
A weekly newsletter from Jack Clark (@jackclarksf) of OpenAI personally covering new research developments, its applications, and larger media coverage.
- Recent example: Amazon now has over 100,000 Kiva robots, NIH releases massive x-ray dataset, and Google creates better grasping robots with GANs
- Subscribe and Archive
Talking Machines 🎧
A podcast focused on delivering clear conversations with experts in the field, insightful discussions of industry topics, and useful answers to your questions.
Started by Google Brain researcher, Ryan Adams.
The Wild Week in AI 📓
A weekly newsletter curated by former Google Brain researcher, Denny Britz. (@DennyBritz)
- Recent example: Information Bottleneck in DL; Faster GPUs on GCE; Unity Machine Learning Agents; and more
- Subscribe and Archive
A loud, colorful, and active YouTuber focused on helping curious beginners and hobbyists hit the ground running.
Reddit 📱
Lively daily discussion around industry news, research papers, starter projects, talent announcements, AMAs, keynotes, and conferences. This is an incredible resource to conduct specific and general searches within.
- /r/machinelearning, /r/learnmachinelearning, and /r/mlquestions
- What does a typical ML architecture look like in production?
- One of my first ML projects — I trained a neural net to ‘write’ rap songs
- Any suggestions for a first ML project?
Fast Forward Labs
Part blog and part newsletter, Fast Forward is a small group of researchers applying machine intelligence to real world problems. They conduct research, build prototypes, and experiment with implementations all while sharing their learnings in public. The content is well suited for individuals with a technical skill set and itch for applying current technologies.
- The Product Possibilities of Interpretability
- Crowdwork for Machine Learning: An Autoethnography
- Practical Deep Learning
Machine Learning is Fun
Adam Geitgey has proven to be one of the more actionable technical resources for software engineers. His public content series and Lynda course are a pleasant and concrete way to get started with meaningful projects.
“Are you tired of reading endless news stories about deep learning and not really knowing what that means? Let’s change that!”
- Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning
- Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks
- Quick Tip: Speed up your Python data processing scripts with Process Pools
People Lists 🐦
- Machine Learning Twitter by QZ reporter Dave Gershgorn
Like many areas of tech, ML twitter can be quite informative to listen in on and interact with. The individuals behind the papers, projects, and companies are keen on intellectual dialogue. A few recent examples of in-depth conversations here, here, and here.
Tools Lists 📝
A complete daily plan for studying to become a machine learning engineer.
This list will continue to develop and there is no end in sight for the interest in actionable knowledge. The rising tide of open source (tools, frameworks, infrastructure) and open data are a critical component for each community.
Part 2 resources for product and business folks here.
