How does the algorithm work? What are the disadvantages and alternatives? And how do we use it in machine learning? How does SMOTE work? SMOTE stands for Synthetic Minority Over-sampling Technique....

How does the algorithm work? What are the disadvantages and alternatives? And how do we use it in machine learning? How does SMOTE work? SMOTE stands for Synthetic Minority Over-sampling Technique....
Word2Vec for text classification Word2Vec is a popular algorithm used for natural language processing and text classification. It is a neural network-based approach that learns distributed...
How does the Deep Belief Network algorithm work? Common applications. Is it a supervised or unsupervised learning method? And how do they compare to CNNs? And how to create an implementation in...
What is the Elman neural network? Elman Neural Network is a recurrent neural network (RNN) designed to capture and store contextual information in a hidden layer. Jeff Elman introduced it in 1990....
Self-attention is the reason transformers are so successful at many NLP tasks. Learn how they work, the different types, and how to implement them with PyTorch in Python. What is self-attention in...
Text normalization is a key step in natural language processing (NLP). It involves cleaning and preprocessing text data to make it consistent and usable for different NLP tasks. The process includes...
What is Part-of-speech (POS) tagging? Part-of-speech (POS) tagging is fundamental in natural language processing (NLP) and can be done in Python. It involves labelling words in a sentence with their...
Transformers Implementations in TensorFlow, PyTorch, Hugging Face and OpenAI's GPT-3 What are transformers in natural language processing? Natural language processing (NLP) is a field of artificial...
What is a Siamese network? It is also commonly known as one or few-shot learning. They are popular because less labelled data is required to train them. Siamese networks are often used to figure out...
Introduction to document clustering and its importance Grouping similar documents together in Python based on their content is called document clustering, also known as text clustering. This...
What is local sensitive hashing? A technique for performing a rough nearest neighbour search in high-dimensional spaces is called local sensitive hashing (LSH). It operates by mapping...
Long Short-Term Memory (LSTM) is a powerful natural language processing (NLP) technique. This powerful algorithm can learn and understand sequential data, making it ideal for analyzing text and...
Convolutional Neural Networks (CNN) are a type of deep learning model that is particularly well-suited for tasks that involve working with structured data, such as images, audio, or text in NLP....
Best RNN For NLP: Elman RNNs, Long short-term memory (LSTM) networks, Gated recurrent units (GRUs), Bi-directional RNNs and Transformer networks What is an RNN? A recurrent neural network (RNN) is...
Encoder, decoder and encoder-decoder transformers are a type of neural network currently at the bleeding edge in NLP. This article explains the difference between these architectures and what they...
What is MinHash? MinHash is a technique for estimating the similarity between two sets. It was first introduced in information retrieval to evaluate the similarity between documents quickly. The...
What is SimHash? Simhash is a technique for generating a fixed-length "fingerprint" or "hash" of a variable-length input, such as a document or a piece of text. It is similar to a hash function and...
This article discusses one of the most valuable tools when analysing textual data in natural language processing — fuzzy string matching. We first discuss what it is, its typical applications and...
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