- Nine Tools I Wish I Mastered Before My PhD in Machine Learning, by Aliaksei Mikhailiuk - Sep 22, 2021.
Whether you are building a start up or making scientific breakthroughs these tools will bring your ML pipeline to the next level.
- What 2 years of self-teaching data science taught me, by Vishnu U - Sep 17, 2021.
Many of us self-learn data science from the very beginning. While continuing to self-learn on demand is crucial, especially after you become a professional, there can be many pitfalls early on for learning the wrong way or missing out on key ideas that are important for the real-world application of data science.
- How Many AI Neurons Does It Take to Simulate a Brain Neuron?, by Jesus Rodriguez - Sep 13, 2021.
A new research shows some shocking answers to that question.
- Smart Ingestion: Using ontology-driven AI, by Prad Upadrashta - Sep 8, 2021.
Imagine data that organizes itself to power your decision-making.
- Math 2.0: The Fundamental Importance of Machine Learning, by Dr. Claus Horn - Sep 8, 2021.
Machine learning is not just another way to program computers; it represents a fundamental shift in the way we understand the world. It is Math 2.0.
- Antifragility and Machine Learning, by Prad Upadrashta - Sep 6, 2021.
Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?
- Behind OpenAI Codex: 5 Fascinating Challenges About Building Codex You Didn’t Know About, by Jesus Rodriguez - Sep 3, 2021.
Some ML engineering and modeling challenges encountering during the construction of Codex.
- How to solve machine learning problems in the real world, by Pau Labarta Bajo - Sep 2, 2021.
Becoming a machine learning engineer pro is your goal? Sure, online ML courses and Kaggle-style competitions are great resources to learn the basics. However, the daily job of a ML engineer requires an additional layer of skills that you won’t master through these approaches.