Navigating natolambert’s writing
You found me on Medium! I use their great search engine optimization to gather followers across a few modalities.
What I do:
- Robot learning research at UC Berkeley (PhD expected fall 2021).
- Write high-signal content on the internet on AI, robotics, and my interests (See below).
- Nerd out on training, longevity, and what it means to be alive.
Support me financially with this link: https://natolambert.medium.com/membership
Where you will find me:
- High signal writing: my Substack on AI, robotics, and automation https://robotic.substack.com/
- Twitter for the knowledge graph @natolambert.
- Medium is where some of my ideas from the first two end up. It works, but the material will be slightly delayed.
Some of my popular articles:
- Crisp python plots based on visualization theory — an introduction to making your plots tell a fuller story.
- How top CS students problem solve — reflections on how the best students I have taught AI to think.
- Three introductions to reinforcement learning (RL) — 3 skills to master, how to start learning (code & math), and gists of deep RL algorithms.
- A series on the fundamentals of reinforcement learning (1, 2, 3, 4).
My messages are always open on twitter, or comment below!
AI Startups & Companies:
“AI” Startups are Data Startups
That being said, riding the wave of deep learning with a unique dataset can be extremely profitable.
Deepmind: the existence proof for RL at scale
The brain is the existence proof for general intelligence — Google’s Deepmind is the proof we are making progress in…
Machine Learning Engineer versus Software Engineer
What is the difference between these two roles and how they think?
Design Optimization with Ax in Python
Humans are terrible at jointly optimizing nonlinear, high-dimensional functions. It’s a fact.
Train a neural network in python to predict robot dynamics
Understand how to work with real robotics data.
Deep RL Case Studies:
Deep RL Case Study: Model-based Planning
Model learning provides structure and interpretability to the field of reinforcement learning.
Deep RL Case Study: Chaotic Gradients
Why do some RL algorithms have notoriously unreliable gradients and what can we do about this in model-based RL.