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✔Target2= 1.831
✔Target3= 1.858
❌ Stop Loss= 1.759
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Invest in projects that don't have FOMO 🔥
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A machine learning algorithm that you should study:
PCA → Principal Component Analysis. An unsupervised learning algorithm.
This picture is Robust PCA, a variation of PCA.
Look at how we can use it to remove the background.
↓
Reinforcement learning agents can learn to cooperate, compete, and communicate in multi-agent settings. New research explores the emergence of micro-economic behaviour as agents learn to produce, consume, and trade resources in simulated worlds: dpmd.ai/Emergent-Barte… 1/
90% of data scientists have no idea how to put their models in production.
Here is a FREE course that solves that:
• Deploy your models
• Track experiments
• Turn notebooks into pipelines
• Monitor models
You can register here for free: airtable.com/shrCb8y6eTbPKw…
This was really a super nice & productive collaboration! And I learned A LOT about computational communication research!
(PS: You can find our paper on "Integrating Machine Learning with Communication Theories to Study the Use of Color & Brightness" here: computationalcommunication.org/ccr/article/vi…)
How should hierarchical RL agents represent the world? Get affordances from the low-level skills, and represent the world in terms of these values -- in terms of what the agent can do! This is the idea @shahdhruv_ & @brian_ichter describe in new blog post: ai.googleblog.com/2022/04/extrac…
How do you build ML models that are efficient and deliver good quality? Popular techniques include distillation & quantization.
It's great to finally see a book on this topic.
4 chapters out with Python code examples. A great read for ML engineers.
efficientdlbook.com
I've just created my first interactive @kaggle notebook! It's a beginner's guide to gradient descent, & answers the question "How does a neural net really work?"
I hope you like it. 😀
kaggle.com/jhoward/how-do…
Visual model-based RL without reconstruction can work well in the presence of distractors, if the information in the learned state is "prioritized". In our ICLR paper, we show how empowerment provides a basis for this "information prioritization": sites.google.com/corp/view/info…
A 🧵:
Define your environment with cog.yaml, then you get:
• A best-practice Dockerfile
• All the CUDA versions resolved
• An HTTP prediction server
• A Redis queue worker
• A standard schema for your model’s inputs/outputs
... and various other handy things!
I've created a new @kaggle notebook: "Getting started with Kaggle, NLP and @huggingface for absolute beginners".
I've never created a Kaggle notebook for beginners before. I hope some of you find it a useful intro to this amazing field!
kaggle.com/code/jhoward/g…
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