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 activa
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...
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...
Here’s a simple implementation of the AdaBoost algorithm using only NumPy:pythonimport numpy as np class DecisionStump: def __init__(self): self.polarity = 1 self.feature_idx = None self.threshold = None self.alpha = None def predict...
K-Nearest Neighbors (KNN) Learning Vector Quantization (LVQ) Support Vector Machines (SVM) Random Forest Boosting Q2: How can we deal with multi-class classification problems ? A:Basically, there are three methods to solve a multi-label classification problem, namely: ...
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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...