Prior work has characterized the challenges of deep learning model development, but as yet we know little about the deep learning model reengineering process and its common challenges. Prior work has examined DL
1.4 Billion Text Credentials Analysis (NLP) Using deep learning and NLP to analyze a large corpus of clear text passwords. Objectives: Train a generative model. Understand how people change their passwords over time: hello123 -> h@llo123 -> h@llo!23. ...
1.4 Billion Text Credentials Analysis (NLP) Using deep learning and NLP to analyze a large corpus of clear text passwords. Objectives: Train a generative model. Understand how people change their passwords over time: hello123 -> h@llo123 -> h@llo!23. ...
and certain progress has been made in the improvement of predictive performance. However, existing deep learning predictors have not fully explored the power of feature representation learning, especially in the discovery of key sequential patterns that ...
1.4 Billion Text Credentials Analysis (NLP)Using deep learning and NLP to analyze a large corpus of clear text passwords.Objectives:Train a generative model.Understand how people change their passwords over time: hello123 -> h@llo123 -> h@llo!23.Disclaimer...
On the other hand, deep learning architectures have also been considered for modeling language patterns in patients with PD. In [26], transliterations of the monologue task of the PC-GITA corpus were used to classify PD patients and HC subjects. Popular natural language processing (NLP) methods...
Based on the availability of label information, deep learning methods can be divided into supervised and unsupervised learning. In supervised learning, given a dataset 𝐷={𝒙𝒏,𝒚𝒏}𝑁𝑛=1D={xn,yn}n=1N of 𝑁N samples where 𝒙x is the observation and 𝒚y is the label, sup...