Hitting the right notes in 2017: AI in a song for Data Scientists
A lot, I mean lots and lots of great articles have been written already about AI’s epic journey in 2017. They all generally agree...
10 Machine Learning Tools to watch in 2018
2017 has been a wonderful year for Machine Learning. Developing smart, intelligent models has now become easier than ever thanks to the extensive research...
How Data Science saved Christmas
It’s the middle of December and it’s shivery cold in the North Pole at -20°C. A fat old man sits on a big brown...
NIPS 2017 Special: Decoding the Human Brain for Artificial Intelligence to make smarter decisions
Yael Niv is an Associate Professor of Psychology at the Princeton Neuroscience Institute since 2007. Her preferred areas of research include human and animal...
NIPS 2017 Special: A deep dive into Deep Bayesian and Bayesian Deep Learning with...
Yee Whye Teh is a professor at the department of Statistics of the University of Oxford and also a research scientist at DeepMind. He...
NIPS 2017 Special: How machine learning for genomics is bridging the gap between research...
Brendan Frey is the founder and CEO of Deep Genomics. He is the professor of engineering and medicine at the University of Toronto. His...
NIPS 2017 Special: 6 Key Challenges in Deep Learning for Robotics by Pieter Abbeel
Pieter Abbeel is a professor at UC Berkeley and a former Research Scientist at OpenAI. His current research focuses on robotics and machine learning...
3 great ways to leverage Structures for Machine Learning problems by Lise Getoor at...
Lise Getoor is a professor in the Computer Science Department, at the University of California, Santa Cruz. She has a PhD in Computer Science...
20 lessons on bias in machine learning systems by Kate Crawford at NIPS 2017
Kate Crawford is a Principal Researcher at Microsoft Research and a Distinguished Research Professor at New York University. She has spent the last decade...
10 machine learning algorithms every engineer needs to know
When it comes to machine learning, it's all about the algorithms. But although machine learning algorithms are the bread and butter of a data...