- Top 20 Recent Research Papers on Machine Learning and Deep Learning - Apr 6, 2017.
Machine learning and Deep Learning research advances are transforming our technology. Here are the 20 most important (most-cited) scientific papers that have been published since 2014, starting with "Dropout: a simple way to prevent neural networks from overfitting".
- Top /r/MachineLearning Posts, November: StarCraft II for AI Research; Google AI Experiments Website; Google in Montreal - Dec 5, 2016.
DeepMind and Blizzard to release StarCraft II as an AI research environment; Google AI Experiments Website; Google opens new Montreal-based AI research lab; Lip Reading Sentences in the Wild; Clean implementations of machine learning algorithms
- New Deep Learning Book Finished, Finalized Online Version Available - Apr 12, 2016.
What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.
- Deep Learning: an Interview with Yoshua Bengio - Mar 8, 2016.
Yoshua Bengio is a renowned figure in the machine learning and specifically deep learning, here is an interview with Yoshua about his thoughts on media interest in the field, future developments and more.
- The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing - Feb 15, 2016.
Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
- Introducing Quora’s Machine Learning Sessions Series - Jan 19, 2016.
Quora is launching a new format for interacting with domain experts and sharing knowledge, and its first topic is Machine Learning. Yoshua Bengio is the first expert, and he is accepting questions now.
- Why Deep Learning Works – Key Insights and Saddle Points - Nov 3, 2015.
A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point.
- Top KDnuggets tweets, Sep 15-21: Top Machine Learning researcher Pedro Domingos new book: The Master Algorithm - Sep 23, 2015.
Top Machine Learning researcher Pedro Domingos new book: The Master #Algorithm; #Dilbert brilliant take on Character; SentimentBuilder - Free Online Natural #Language Processing Tool.
- Top 10 Quora Machine Learning Writers and Their Best Advice - Sep 18, 2015.
Top Quora machine learning writers give their advice on pursuing a career in the field, academic research, and selecting and using appropriate technologies.
- Talking Machine – 3 Deep Learning Gurus Talk about History and Future, part 2 - Mar 26, 2015.
Key ideas from a podcast with Deep Learning gurus Geoff Hinton, Yoshua Bengio, and Yann LeCun, where they explain the power of distributed representation and also propose a new open paper review process.
- Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1 - Mar 25, 2015.
An recent interview from the talking machine podcast with three deep learning experts. They talked about the neural network winter and its renewal.
- Top /r/MachineLearning posts, January - Feb 13, 2015.
Talking Machines, SVM lectures, a new Stanford statistical learning online course, and a listing of open-source datasets top the most popular Reddit posts on /r/MachineLearning for the month of January.
- Why unsupervised learning is more robust to adversarial distortions - Jan 30, 2015.
Yoshua Bengio, a leading expert on Deep Learning, explains why good unsupervised learning should be much more robust to adversarial distortions than supervised learning.
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
- Deep Learning – important resources for learning and understanding - Aug 21, 2014.
New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.