def knn_model(train): return train # Make a prediction with weights def perceptron_predict(model, row): activation = model[0] for i in range(len(row)-1): activation += model[i + 1] * row[i] return 1.0 if activation >= 0.0 else 0.0 # Estimate Perceptron weights using stochastic gr...
Registered for the Microsoft AI challenge to improve Bing's suggestion box answers using DL models Re-doing the plan for the next 50 days to get the most done from this challenge Day 51 (29-10-18) Sentiment classification An implementation from Andrew Trask's blog about sentiment classification...
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Design an artificial neural network model for generating text by using LSTM, I use Embedding layer for embedding word from text to feature vector and find relationships among them In the final layer, I use Dense layer with Softmax activation function for classifying which word has the highest pr...
Bugs: New code that has few users is more likely to have bugs, even with a skilled programmer and unit tests. Using a standard library can reduce the likelihood of having bugs in the algorithm implementation. Non-intuitive Leaps: Some algorithms rely on non-intuitive jumps in reasoning or lo...