Deep Learning, NLP, NMT, AI, ML
Python tutorials
Welcome to the first article of the “Machine Learning Deep Dive” biweekly series. Each article will consist of a theoretical summary based on Ethem Alpaydın’s Machine Learning book. For each method, there will be a separate GitHub repository containing sample codes and examples prepared from the scratch and/or using the open-source Python libraries. Wish you pleasant reading!
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In this article, we will discuss how to implement sentiment analysis on a raw tweets dataset in python, find the polarity scores to classify the sentiments, and train a Long short-term memory (LSTM) and Convolutional Neural Network to predict the sentiment polarity and compare the results. Reference
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VECTOR EMBEDDING : it’s a technique for learning numerical representations for token (word) that approximate their lexical meaning.these representations are learned by observed words in their context of occurrence in large volumes of data.
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It seems like Machine learning and Deep Learning are everywhere these days. AI can do anything! From writing essays to cheat tests to generating to having an infinite image generating machine running on your local machine, the possibilities are endless. So it should come as no surprise that you can use deep learning to predict the outcome of major sports tournaments.
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K — means clustering is a centroid-based unsupervised machine learning algorithm. Unsupervised learning uses the machine learning algorithm to analyze unlabelled data and find hidden patterns without human intervention. It’s clear from the name itself that K-means is a cluster-based algorithm. Clustering is a technique where we can group together a set
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