News & Articles
Applying Deep Learning to Real World Problems
The rise of artificial intelligence in recent years is grounded in the success of deep learning. This creates the potential for many disruptive new businesses leveraging deep learning to solve real-world problems.
medium.com
OpenAI Baselines: DQN
"We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results."
openai.com
Papers & Tutorials
Progressive Neural Networks
The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features.
arxiv.org
The Cramer Distance as a Solution to Biased Wasserstein Gradients
"In this paper we describe three natural properties of probability divergences that reflect requirements from machine learning: sum invariance, scale sensitivity, and unbiased sample gradients. The Wasserstein metric possesses the first two properties but, unlike the Kullback-Leibler divergence, does not possess the third."
arxiv.org
Tips for Beginners
AI Learning Path
It is tough for a software developer to decide what to study and at what depth. Learn. Learn which software technology and methods you should invest your time, energy and possibly money into for educational materials, courses, and certificates.
ivan-farkas.com
Introduction to Probabilistic Modelling and Machine Learning
Machine Learning and Nonparametric Bayesian Statistics by prof. Zoubin Ghahramani. These lectures are part of the Visiting Professor Programme co-financed by the European Union within Development of the Teaching & Research Capacity of Young Academic Staff at Wrocław University of Technology.
youtube.com
