Deep learning has become a ubiquitous technology to improve machine intelligence.

Deep learning has become a ubiquitous technology to improve machine intelligence.

If you spend more on coffee than IT security, you will be hacked! Warned U.S. Cybersecurity Czar Richard Clarke, speaking at RSA Conference. This quote would make a great bumper sticker if it weren't for network attacks. According to research by IBM, it takes 280 days to find and contain the average cyberattack, while the average attack costs $3.86 million. But what are network attacks, and how can we leverage a next-gen search tool like Jina to mitigate our exposure to the threat?

Keras-surgeon provides simple methods for modifying trained Keras models. Keras-surgeon is compatible with any model architecture. Any number of layers can be modified in a single traversal of the network. These kinds of modifications are sometimes known as network surgery which inspired the name of this package. The operations module contains simple methods to perform network surgery on a single layer within a model.

Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of 108 and 17.7 respectively, proving that it outperforms the recent pruning method by considerable margins.