List of Interview Questions BERT Interview Questions (NLP) In this article, we will go over various questions that cover the fundamentals and inner workings of the BERT model.
Natural Language Processing (NLP) NLP Project: Compare Text Summarization Models In this article, we will go over the basics of Text Summarization, the different approaches to generating automatic summaries, some of the real world applications of Text Summarization, and finally, we will compare various Text Summarization models with the help of ROUGE.
Natural Language Processing (NLP) Text Summarization Interview Questions (NLP) In this article, we will go over 70 questions that cover everything from the very basics of Text Summarization to the evaluation of summarized pieces of text using various metrics.
Natural Language Processing (NLP) Types of NLP models Natural Language Processing (NLP) refers to a branch of Artificial Intelligence (AI) in Computer Science that gives computers the ability to analyze and interpret human language.
Machine Learning (ML) Text Summarization using Transformers In this article, we will learn about the fundamentals of Text Summarization, some of the different ways in which we can summarize text, Transformers, the BART model, and finally, we will practically implement some of these concepts.
Machine Learning (ML) Embeddings in BERT We will see what is BERT (bi-directional Encoder Representations from Transformers). How the BERT actually works and what are the embeddings in BERT that make it so special and functional compared to other NLP learning techniques.
Natural Language Processing (NLP) Word Embedding [Complete Guide] We have explained the idea behind Word Embedding, why it is important, different Word Embedding algorithms like Embedding layers, word2Vec and other algorithms.
Natural Language Processing (NLP) Why SpaCy over NLTK? We listed 10 aspects where spaCy shines better than NLTK. It also includes information when NLTK outsmarts spaCy.
Machine Learning (ML) Applications of NLP: Extraction from PDF, Language Translation and more In this, we have explored core NLP applications such as text extraction, language translation, text classification, question answering, text to speech, speech to text and more.
Machine Learning (ML) Applications of NLP: Text Generation, Text Summarization and Sentiment Analysis In this article, we have explored 3 core NLP applications such as Text Generation using GPT models, Text summarization and Sentiment Analysis.
Machine Learning (ML) ALBERT (A Lite BERT) NLP model ALBERT stands for A Lite BERT and is a modified version of BERT NLP model. It builds on three key points such as Parameter Sharing, Embedding Factorization and Sentence Order Prediction (SOP).
Machine Learning (ML) Different core topics in NLP (with Python NLTK library code) In this, we have covered different NLP tasks/ topics such as Tokenization of Sentences and Words, Stemming, Lemmatization, POS Tagging, Named Entity Relationship and more.
Machine Learning (ML) XLNet, RoBERTa, ALBERT models for Natural Language Processing (NLP) We have explored some advanced NLP models such as XLNet, RoBERTa and ALBERT and will compare to see how these models are different from the fundamental model i.e BERT.
Machine Learning (ML) LSTM & BERT models for Natural Language Processing (NLP) The fundamental NLP model that is used initially is LSTM model but because of its drawbacks BERT became the favored model for the NLP tasks.
Machine Learning (ML) The Idea of Indexing in NLP for Information Retrieval We have explored the fundamental ideas for Information Retrieval that is Indexing Data. We have covered various types of indexes like Term document incidence matrix, Inverted index, boolean queries, dynamic and distributed indexing, distributed indexing and Dynamic Index.
Machine Learning (ML) Heaps' law in NLP for Frequency of Words Heap's Law in NLP is a relation between the number of unique words to the total number of words in a document. It is, also, known as Herdan's law.
Machine Learning (ML) Zipf's Law in NLP According to Zipf's law, the frequency of a given word is dependent on the inverse of it's rank . Zipf's law is one of the many important laws that plays a significant part in natural language processing.
Machine Learning (ML) Byte Pair Encoding for Natural Language Processing (NLP) Byte Pair Encoding is originally a compression algorithm that was adapted for NLP usage. Byte Pair Encoding comes in handy for handling the vocabulary issue through a bottom-up process.
Machine Learning (ML) A Deep Learning Approach for Native Language Identification (NLI) Native language identification (NLI) is the task of determining an author's native language based only on their writings or speeches in a second language. In this article, we will implement a model to identify native language of the author.
Machine Learning (ML) Complete Guide on different Spell Correction techniques in NLP This is the complete Guide on different Spell Correction techniques in Natural Language Processing (NLP) where we have explored approximate string matching techniques, coarse search, fine search, symspell, Seq2Seq along with code demonstration.
Machine Learning (ML) Different Word Representations We have discussed the different word representations such as distributional representation, clustering based representation and distributed representation with several sub-types for each representation.
Machine Learning (ML) Topic Modeling using Non Negative Matrix Factorization (NMF) Non-Negative Matrix Factorization is a statistical method to reduce the dimension of the input corpora. It uses factor analysis method to provide comparatively less weightage to the words with less coherence.
Machine Learning (ML) Sentiment Analysis Techniques Sentiment Analysis is the application of analyzing a text data and predict the emotion associated with it. This is a challenging Natural Language Processing problem and there are several established approaches which we will go through.
Machine Learning (ML) Text Summarization using RNN Encoder Decoder RNN (Recurrent neural network) model is used in order to overcome all the limits faced by the NLP for text summarization such as getting a short and accurate summary.
Machine Learning (ML) Latent Dirichlet Allocation (LDA) Latent Dirichlet Allocation (LDA) is used as a topic modelling technique that is it can classify text in a document to a particular topic. It uses Dirichlet distribution to find topics for each document model