- Neural Networks seem to follow a puzzlingly simple strategy to classify images - Mar 5, 2019.
We explain why state-of-the-art Deep Neural Networks can still recognize scrambled images perfectly well and how this helps to uncover a puzzlingly simple strategy that DNNs seem to use to classify natural images.
- Deep Multi-Task Learning – 3 Lessons Learned - Feb 15, 2019.
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
- Top 8 Free Must-Read Books on Deep Learning - Apr 10, 2018.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
- Tensorflow Tutorial: Part 1 – Introduction - Sep 21, 2017.
Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.
- 3 practical thoughts on why deep learning performs so well - Feb 3, 2017.
Why does Deep Learning perform better than other machine learning methods? We offer 3 reasons: integration of integration of feature extraction within the training process, collection of very large data sets, and technology development.
- 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
- Deep Learning cleans podcast episodes from ‘ahem’ sounds - Nov 8, 2016.
“3.5 mm audio jack… Ahem!!” where did you hear that? ;) Well, this post is not about Google Pixel vs iPhone 7, but how to remove ugly “Ahem” sound from a speech using deep convolutional neural network. I must say, very interesting read.
- 3 Thoughts on Why Deep Learning Works So Well - Aug 10, 2016.
While answering a posed question in his recent Quora Session, Yann LeCun also shared 3 high-level thoughts on why deep learning works so well.
- Stochastic Depth Networks Accelerate Deep Network Training - Apr 7, 2016.
Read about the presentation and overview of a new deep neural network architectural method, and the response to some strong reaction that it brought about.
- Beyond the Fence, and the Advent of the Creative Machines - Jan 25, 2016.
Creative machines have been making their influence felt for some time, but an upcoming stage production challenges preconceived notions of what art is.
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- Deep Forger: Art Forgery Meets Deep Neural Nets - Dec 1, 2015.
The past year has seen deep learning make exceptional advances in imaging, perhaps most notably with Google's Deep Dream. See how a clever Twitter bot employs deep neural nets to paint images in the style of famous painters.
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- Why Does Deep Learning Work? - Jun 23, 2015.
Many researchers recently trying to open the “black-box” of the deep learning. Here we summarize these efforts of how neural nets of deep learning are evolve and how Spin Funnel and deep learning are related.
- The Myth of Model Interpretability - Apr 27, 2015.
Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?
- Juergen Schmidhuber AMA: The Principles of Intelligence and Machine Learning - Mar 9, 2015.
Jürgen Schmidhuber, pioneer in innovating Deep Neural Networks, answers questions on open code, general problem solvers, quantum computing, PhD students, online courses, and the neural network research community in this Reddit AMA.
- Deep Learning can be easily fooled - Jan 14, 2015.
It is almost impossible for human eyes to label the images below to be anything but abstract arts. However, researchers found that Deep Neural Network will label them to be familiar objects with 99.99% confidence. The generality of DNN is questioned again.