Article
This is the 19th article in my series of articles on Python for NLP. From the last few articles, we have been exploring fairly advanced NLP concepts based on deep learning techniques. In the last article, we saw how to create a text classification model trained using multiple inputs of...
Usman Malik
This is the 18th article in my series of articles on Python for NLP. In my previous article, I explained how to create a deep learning-based movie sentiment analysis model using Python's Keras library. In that article, we saw how we can perform sentiment analysis of user reviews regarding different...
Developing symbols which have some value is a trait unique to humans. Recognizing these symbols and understanding the letters on an image is absolutely normal for us. We never really grasp letters like computers do, we completely base our ability to read them on our sight. On the other hand,...
David Landup
Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem. Using transfer learning can dramatically speed up the rate of deployment for an app you are designing,...
Dan Nelson
This is the 17th article in my series of articles on Python for NLP. In the last article, we started our discussion about deep learning for natural language processing. The previous article was focused primarily towards word embeddings, where we saw how word embeddings can be used to convert text...
Genetic algorithms are a part of a family of algorithms for global optimization called Evolutionary Computation, which is comprised of artificial intelligence metaheuristics with randomization inspired by biology. In the previous article, Introduction to Genetic Algorithms in Java, we've covered the terminology and theory behind all of the things you'd...
Darinka Zobenica
Genetic algorithms are a part of a family of algorithms for global optimization called Evolutionary Computation, which is comprised of artificial intelligence metaheuristics with randomization inspired by biology. Wow, words can really be arranged in any order! But hang in there, we'll break this down: Global optimization is a branch...
Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been...
Generative models are a family of AI architectures whose aim is to create data samples from scratch. They achieve this by capturing the data distributions of the type of things we want to generate. These kinds of models are being heavily researched, and there is a huge amount of hype...
Daniele Paliotta
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