Attention is like tf-idf for deep learning. Both attention and tf-idf boost the importance of some words over others. But while tf-idf weight vectors are static for a set of documents, the attention weight vectors will adapt depending on the particular classification objective. Attention derives larger weights for those words that are influencing the classification objective, thus opening a window into the decision making process with in the deep learning blackbox…
Convolutional layers and their cousins the pooling layers are examined for shape modification and parameter counts as functions of layer parameters in Keras/Tensorflow…
Formulae for trainable parameter counts are developed for a few popular layers as function of layer parameters and input characteristics. The results are then reconciled with what Keras reports upon running the model…
Feature space cracking new data introduces potentially useful new classes if detected. Spurts in the rate of increase of new data points with a less than acceptable classification confidence, indicate that new data zones are being carved out in the feature space…
Concept drift is a drift of labels with time for the essentially the same data. It leads to the divergence of decision boundary for new data from that of a model built from earlier data/labels. Scoring randomly sampled new data can detect the drift allowing us to trigger the expensive re-label/re-train tasks on an as needed basis…
Logistic Regression has traditionally been used as a linear classifier, i.e. when the classes can be separated in the feature space by linear boundaries. That can be remedied however if we happen to have a better idea as to the shape of the decision boundary…
In our last article, we were getting some really good results with CNN when we used a custom text corpus. But will CNN manage to hold onto its lead when it competes with SVM in the battle of sentiment analysis, let’s find that out…
SVM with Tf-idf vectors edges out LSTM in quality and performance for classifying the 20-newsgroups text corpus.
Earlier with the bag of words approach we were getting some really good text classification results. But will that hold, when we take into consideration the sequence of words? There is only one way to find out, let’s get right into the action, where we are doing a head on comparison of traditional approach (Naive Bayes) with a modern neural based one (CNN).
For generic text, word bag approaches are very efficient at text classification. For a binary text classification task studied here, LSTM working with word sequences is on par in quality with SVM using tf-idf vectors. But performance is a different matter…
