Deep Learning Discover the Revolutionary Instruct GPT Instruct GPT, or simply Instruct, is a powerful tool that allows users to fine-tune the language generation capabilities of the GPT (Generative Pre-trained Transformer) model.
Deep Learning DistilBERT: The Compact NLP Powerhouse DistilBERT is a smaller, faster, and lighter version of the popular BERT (Bidirectional Encoder Representations from Transformers) model developed by Hugging Face. It was introduced in 2019.
Deep Learning Battle of the Titans: Comparing BART and BERT in NLP In this article, we have explored the differences between two state of the art NLP models namely BERT and BART.
Deep Learning GPT-3.5 model architecture GPT-3.5 model is a fined-tuned version of the GPT3 (Generative Pre-Trained Transformer) model. GPT-3.5 was developed in January 2022 and has 3 variants each with 1.3B, 6B and 175B parameters. The main feature of GPT-3.5 was to eliminate toxic output to a certain extend.
Deep Learning Self-attention in Transformer Today we will discuss one of the revolutionary concepts in the artificial intelligence sector not only in Natural Language Processing but also nowadays in the Computer Vision, which is the Transformers and the heart of it Self-Attention.
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.