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May 7 · Issue #14 · View online
Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at https://nibble.ai/
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In this week’s edition: decentralized ML, operationalization, Stanford’s CS224N winter’s videos, O'Reilly’s Python books for a bargain, and more!
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Why Decentralizing Machine Learning Matters for Your Startup
The idea of decentralized machine learning could increase ML adoption in previously infeasible applications, like in the healthcare industry.
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Debunking The Myths And Reality Of Artificial Intelligence
What makes or breaks AI adoption in business is not the AI academic methods and algorithms or the technology platforms built around them but the way we adopt, architect and integrate them in business solutions and industrial products.
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Moving AI from PoC Stage to Production
Your ML proof-of-concept is the first step of a long process. You need to look beyond it to issues that arise when you scale it to a real-scale production system .
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Forget about artificial intelligence, extended intelligence is the future
A constructive criticism against the growing singularity movement.
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Videos for CS224N Winter 2019
Stanford just released the videos for their 2019 winter session of CS224N: Natural Language Processing with Deep Learning
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AI Notes: Initializing neural networks
A visual tutorial by deeplearning.ai (Andrew Ng) about how to initialize weights of a neural network.
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Data Science for Startups: Containers
One of the skills that is becoming more in demand for data scientists is the ability to reproduce analyses. Having code and scripts that only work on your machine is no longer sustainable. You need to be able to share your work and have other teams be able to repeat your results. This blog post is about encouraging the reuse of analyses via containers, which means that your work can transfer.
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Humble Book Bundle: Python by O'Reilly
13 Python O'Reilly books for $15! Includes Fluent Python, Python Data Science Handbook and Think Bayes.
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