Tagmachine-learning
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Twitter Sentiment Analysis using combined LSTM-CNN Models
Combining CNNs and LSTMs for tweet sentiment analysis — the intuition behind each network, two stacked architectures (CNN-LSTM vs. LSTM-CNN), and why ordering the LSTM first wins, trained on 1.5M+ labeled tweets.
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5-Min Tutorial: K-Means Clustering In Python
A five-minute tutorial on K-Means clustering in Python — with a live, animated visualizer that shows exactly how the clusters get selected: random centers, assign-to-nearest, recenter, repeat to convergence.
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SoundCloud Spam Analysis
Crawling 20 million SoundCloud users with a Docker crawler army (and a "borrowed" public API key), then using K-Means clustering and a Keras neural net to find and predict spam accounts — with dataset, paper, code, and PDF at the end.
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Twitter Sentiment Analysis with Neural Networks
Hand-coding a Feed-Forward Neural Network from scratch to classify tweets as positive or negative, comparing Bayesian probabilities, Keras word embeddings, and a hand-built feature vector (~70% accuracy) against a Keras NN.
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