Liquid network has proven more efficient than other state-of-the-art time series algorithms to accurately predict future values in datasets.
The researchers at the Massachusetts Institute of Technology (MIT) have developed ‘liquid network’, a neural network that can learn on the job.
If deep learning is a super power, then turning theories from a paper to usable code is a hyper power. Why should I learn to implement machine learning research papers?
Looking to attend an AI event or two this year? Below ... Here are the top 22 machine learning conferences in 2020: ... Start Date: June 10th, 2020 ... Join more than 400 other data-heads in 2020 and propel your career forward. ... They feature 30+ data science sessions crafted to bring specialists in different ...
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Deep Q-Networks have revolutionized the field of Deep Reinforcement Learning, but the technical prerequisites for easy experimentation have barred newcomers until now.
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.