Search results for word embeddings

Found 203 documents, 5951 searched:

• Content-Based Recommendation System using Word Embeddings

  This article explores how average Word2Vec and TF-IDF Word2Vec can be used to build a recommendation engine.

  https://www.kdnuggets.com/2020/08/content-based-recommendation-system-word-embeddings.html

• Word Embeddings in NLP and its Applications

  Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.

  https://www.kdnuggets.com/2019/02/word-embeddings-nlp-applications.html

• Word Embeddings & Self-Supervised Learning, Explained

  There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.

  https://www.kdnuggets.com/2019/01/burkov-self-supervised-learning-word-embeddings.html

• On the contribution of neural networks and word embeddings in Natural Language Processing

  In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.

  https://www.kdnuggets.com/2018/05/contribution-neural-networks-word-embeddings-natural-language-processing.html

• Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors

  In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.

  https://www.kdnuggets.com/2018/07/text-classification-lstm-cnn-pre-trained-word-vectors.html

• The Ultimate Guide To Different Word Embedding Techniques In NLP

  A machine can only understand numbers. As a result, converting text to numbers, called embedding text, is an actively researched topic. In this article, we review different word embedding techniques for converting text into vectors.

  https://www.kdnuggets.com/2021/11/guide-word-embedding-techniques-nlp.html

• Training BPE, WordPiece, and Unigram Tokenizers from Scratch using Hugging Face

  Comparing the tokens generated by SOTA tokenization algorithms using Hugging Face's tokenizers package.

  https://www.kdnuggets.com/2021/10/bpe-wordpiece-unigram-tokenizers-using-hugging-face.html

• Word Embedding Fairness Evaluation

  With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.

  https://www.kdnuggets.com/2020/08/word-embedding-fairness-evaluation.html

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.html

• Lemma, Lemma, Red Pyjama: Or, doing words with AI

  If we want a machine learning model to be able to generalize these forms together, we need to map them to a shared representation. But when are two different words the same for our purposes? It depends.

  https://www.kdnuggets.com/2019/10/lemma-lemma-red-pyjama-words-ai.html

• Word Morphing – an original idea

  In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.

  https://www.kdnuggets.com/2018/11/word-morphing-original-idea.html

• Word Vectors in Natural Language Processing: Global Vectors (GloVe)

  A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.

  https://www.kdnuggets.com/2018/08/word-vectors-nlp-glove.html

• Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks

  The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.

  https://www.kdnuggets.com/2018/04/robust-word2vec-models-gensim.html

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW)

  The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).

  https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-cbow.html

• Training and Visualising Word Vectors

  In this tutorial I want to show how you can implement a skip gram model in tensorflow to generate word vectors for any text you are working with and then use tensorboard to visualize them.

  https://www.kdnuggets.com/2018/01/training-visualising-word-vectors.html

• Search Millions of Documents for Thousands of Keywords in a Flash

  We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.

  https://www.kdnuggets.com/2017/09/search-millions-documents-thousands-keywords.html

• Going deeper with recurrent networks: Sequence to Bag of Words Model

  Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.

  https://www.kdnuggets.com/2017/08/deeper-recurrent-networks-sequence-bag-words-model.html

• The Human Vector: Incorporate Speaker Embeddings to Make Your Bot More Powerful

  One of the many ways in which bots can fail is by their (lack of) persona. Learn how speaker embeddings can help with this problem, and can help improve the persona of your bot.

  https://www.kdnuggets.com/2016/09/human-vector-incorporate-speaker-embedding-powerful-bot.html

• Deep Learning Transcends the Bag of Words

  Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.

  https://www.kdnuggets.com/2015/12/deep-learning-outgrows-bag-words-recurrent-neural-networks.html

• Why Deep Learning is perfect for NLP (Natural Language Processing)">Why Deep Learning is perfect for NLP (Natural Language Processing)

  Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.

  https://www.kdnuggets.com/2018/04/why-deep-learning-perfect-nlp-natural-language-processing.html

• Semantic Search with Vector Databases

  Leverage the latest technology to improve our search engine capabilities.

  https://www.kdnuggets.com/semantic-search-with-vector-databases

• The Ultimate Roadmap to Becoming Specialised in The Tech Industry

  There is more than one route that you can take to be a competitive tech professional.

  https://www.kdnuggets.com/the-ultimate-roadmap-to-becoming-specialised-in-the-tech-industry

• Large Language Models Explained in 3 Levels of Difficulty

  Simple explanations, no matter what your level is right now.

  https://www.kdnuggets.com/large-language-models-explained-in-3-levels-of-difficulty

• Kickstart Your NLP Journey with These 5 Free Courses

  Want to transition into the NLP field without wanting to spend a buck? You can - with these 5 courses.

  https://www.kdnuggets.com/kickstart-your-nlp-journey-with-these-5-free-courses

• Generative AI Key Terms Explained

  This article introduces and explains key terms important to generative AI, and links to additional resources to learn more.

  https://www.kdnuggets.com/generative-ai-key-terms-explained

• 7 Steps to Running a Small Language Model on a Local CPU

  Discover how to run a small language model on your local CPU in just seven easy steps.

  https://www.kdnuggets.com/7-steps-to-running-a-small-language-model-on-a-local-cpu

• 7 Steps to Mastering Natural Language Processing

  Want to learn all about Natural Language Processing (NLP)? Here is a 7 step guide to help you go from the fundamentals of machine learning and Python to Transformers, recent advances in NLP, and beyond.

  https://www.kdnuggets.com/7-steps-to-mastering-natural-language-processing

• Introduction to Natural Language Processing

  An overview of Natural Language Processing (NLP) and its applications.

  https://www.kdnuggets.com/introduction-to-natural-language-processing

• Unveiling Unsupervised Learning

  Explore the unsupervised learning paradigm. Familiarize yourself with the key concepts, techniques, and popular unsupervised learning algorithms.

  https://www.kdnuggets.com/unveiling-unsupervised-learning

• 7 Projects Built with Generative AI

  Learn how to build a strong portfolio with personal projects using Generative AI. This will help you to stand out from the crowd!

  https://www.kdnuggets.com/2023/08/7-projects-built-generative-ai.html

• LangChain + Streamlit + Llama: Bringing Conversational AI to Your Local Machine

  Integrating Open Source LLMs and LangChain for Free Generative Question Answering (No API Key required).

  https://www.kdnuggets.com/2023/08/langchain-streamlit-llama-bringing-conversational-ai-local-machine.html

• A Guide to Top Natural Language Processing Libraries

  Natural Language Processing is one of the hottest areas of research. While NLP tasks may seem a bit complicated at first, they can be made easier by using the right tools. This article covers a list of the top 6 NLP Libraries that can save you time and effort.

  https://www.kdnuggets.com/2023/04/guide-top-natural-language-processing-libraries.html

• Multimodal Models Explained

  Unlocking the Power of Multimodal Learning: Techniques, Challenges, and Applications.

  https://www.kdnuggets.com/2023/03/multimodal-models-explained.html

• Concepts You Should Know Before Getting Into Transformers

  Learn about Input Embedding, Positional Encoding, Scaled Dot-Product Attention, Residual Connections, Mask, and Softmax function.

  https://www.kdnuggets.com/2023/01/concepts-know-getting-transformer.html

• Zero-shot Learning, Explained

  How you can train a model to learn and predict unseen data?

  https://www.kdnuggets.com/2022/12/zeroshot-learning-explained.html

• Top 5 NLP Cheat Sheets for Beginners to Professional

  The cheat sheets cover various NLP techniques, tasks, algorithms, frameworks, and analytics.

  https://www.kdnuggets.com/2022/12/top-5-nlp-cheat-sheets-beginners-professional.html

• Getting Started with spaCy for NLP

  In this blog, we will explore how to get started with spaCy right from the installation to explore the various functionalities it provides.

  https://www.kdnuggets.com/2022/11/getting-started-spacy-nlp.html

• Getting Started with Automated Text Summarization

  This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.

  https://www.kdnuggets.com/2019/11/getting-started-automated-text-summarization.html

• The ABCs of NLP, From A to Z

  There is no shortage of tools today that can help you through the steps of natural language processing, but if you want to get a handle on the basics this is a good place to start. Read about the ABCs of NLP, all the way from A to Z.

  https://www.kdnuggets.com/2022/10/abcs-nlp-a-to-z.html

• Data Representation for Natural Language Processing Tasks

  In NLP we must find a way to represent our data (a series of texts) to our systems (e.g. a text classifier). As Yoav Goldberg asks, "How can we encode such categorical data in a way which is amenable for us by a statistical classifier?" Enter the word vector.

  https://www.kdnuggets.com/2018/11/data-representation-natural-language-processing.html

• 10 Cheat Sheets You Need To Ace Data Science Interview

  The only cheat you need for a job interview and data professional life. It includes SQL, web scraping, statistics, data wrangling and visualization, business intelligence, machine learning, deep learning, NLP, and super cheat sheets.

  https://www.kdnuggets.com/2022/10/10-cheat-sheets-need-ace-data-science-interview.html

• Is Domain Knowledge Important for Machine Learning?

  If you incorporate domain knowledge into your architecture and your model, it can make it a lot easier to explain the results, both to yourself and to an outside viewer. Every bit of domain knowledge can serve as a stepping stone through the black box of a machine learning model.

  https://www.kdnuggets.com/2022/07/domain-knowledge-important-machine-learning.html

• Design Patterns in Machine Learning for MLOps

  This article outlines some of the most common design patterns encountered when creating successful Machine Learning solutions.

  https://www.kdnuggets.com/2022/02/design-patterns-machine-learning-mlops.html

• Transfer Learning for Image Recognition and Natural Language Processing

  Read the second article in this series on Transfer Learning, and learn how to apply it to Image Recognition and Natural Language Processing.

  https://www.kdnuggets.com/2022/01/transfer-learning-image-recognition-natural-language-processing.html

• Using Datawig, an AWS Deep Learning Library for Missing Value Imputation

  A lot of missing values in the dataset can affect the quality of prediction in the long run. Several methods can be used to fill the missing values and Datawig is one of the most efficient ones.

  https://www.kdnuggets.com/2021/12/datawig-aws-deep-learning-library-missing-value-imputation.html

• Inside recommendations: how a recommender system recommends

  We describe types of recommender systems, more specifically, algorithms and methods for content-based systems, collaborative filtering, and hybrid systems.

  https://www.kdnuggets.com/2021/11/recommendations-recommender-system.html

• Dream Come True: Building websites by thinking about them

  From the mind to the computer, make websites using your imagination!

  https://www.kdnuggets.com/2021/11/dream-come-true-allennlp-hacks-21.html

• Introducing TensorFlow Similarity

  TensorFlow Similarity is a newly-released library from Google that facilitates the training, indexing and querying of similarity models. Check out more here.

  https://www.kdnuggets.com/2021/09/introducing-tensorflow-similarity.html

• Text Preprocessing Methods for Deep Learning

  While the preprocessing pipeline we are focusing on in this post is mainly centered around Deep Learning, most of it will also be applicable to conventional machine learning models too.

  https://www.kdnuggets.com/2021/09/text-preprocessing-methods-deep-learning.html

• How Machine Learning Leverages Linear Algebra to Solve Data Problems

  Why you should learn the fundamentals of linear algebra.

  https://www.kdnuggets.com/2021/09/machine-learning-leverages-linear-algebra-solve-data-problems.html

• Linear Algebra for Natural Language Processing

  Learn about representing word semantics in vector space.

  https://www.kdnuggets.com/2021/08/linear-algebra-natural-language-processing.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• How to Create Unbiased Machine Learning Models

  In this post we discuss the concepts of bias and fairness in the Machine Learning world, and show how ML biases often reflect existing biases in society. Additionally, We discuss various methods for testing and enforcing fairness in ML models.

  https://www.kdnuggets.com/2021/07/create-unbiased-machine-learning-models.html

• What is Neural Search?

  And how to get started with it with no prior experience in Machine Learning.

  https://www.kdnuggets.com/2021/05/what-neural-search.html

• Deep Learning Recommendation Models (DLRM): A Deep Dive

  The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.

  https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html

• How Reading Papers Helps You Be a More Effective Data Scientist"> How Reading Papers Helps You Be a More Effective Data Scientist

  By reading papers, we were able to learn what others (e.g., LinkedIn) have found to work (and not work). We can then adapt their approach and not have to reinvent the rocket. This helps us deliver a working solution with lesser time and effort.

  https://www.kdnuggets.com/2021/02/reading-papers-effective-data-scientist.html

• Essential Math for Data Science: Scalars and Vectors

  Linear algebra is the branch of mathematics that studies vector spaces. You’ll see how vectors constitute vector spaces and how linear algebra applies linear transformations to these spaces. You’ll also learn the powerful relationship between sets of linear equations and vector equations.

  https://www.kdnuggets.com/2021/02/essential-math-data-science-scalars-vectors.html

• Topic Modeling with BERT

  Leveraging BERT and TF-IDF to create easily interpretable topics.

  https://www.kdnuggets.com/2020/11/topic-modeling-bert.html

• Roadmap to Natural Language Processing (NLP)">Roadmap to Natural Language Processing (NLP)

  Check out this introduction to some of the most common techniques and models used in Natural Language Processing (NLP).

  https://www.kdnuggets.com/2020/10/roadmap-natural-language-processing-nlp.html

• Text Mining with R: The Free eBook">Text Mining with R: The Free eBook

  This freely-available book will show you how to perform text analytics in R, using packages from the tidyverse.

  https://www.kdnuggets.com/2020/10/text-mining-r-free-ebook.html

• A Deep Dive Into the Transformer Architecture – The Development of Transformer Models

  Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.

  https://www.kdnuggets.com/2020/08/transformer-architecture-development-transformer-models.html

• Awesome Machine Learning and AI Courses">Awesome Machine Learning and AI Courses

  Check out this list of awesome, free machine learning and artificial intelligence courses with video lectures.

  https://www.kdnuggets.com/2020/07/awesome-machine-learning-ai-courses.html

• Chatbots in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about chatbots and the ways they are used.

  https://www.kdnuggets.com/2020/05/chatbots-nutshell.html

• Peer Reviewing Data Science Projects">Peer Reviewing Data Science Projects

  In any technical development field, having other practitioners review your work before shipping code off to production is a valuable support tool to make sure your work is error-proof. Even through your preparation for the review, improvements might be discovered and then other issues that escaped your awareness can be spotted by outsiders. This peer scrutiny can also be applied to Data Science, and this article outlines a process that you can experiment with in your team.

  https://www.kdnuggets.com/2020/04/peer-reviewing-data-science-projects.html

• How To Build Your Own Feedback Analysis Solution

  Automating the analysis of customer feedback will sound like a great idea after reading a couple hundred reviews. Building an NLP solution to provide in-depth analysis of what your customers are thinking is a serious undertaking, and this guide helps you scope out the entire project.

  https://www.kdnuggets.com/2020/03/build-feedback-analysis-solution.html

• Intent Recognition with BERT using Keras and TensorFlow 2

  TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!

  https://www.kdnuggets.com/2020/02/intent-recognition-bert-keras-tensorflow.html

• NLP Year in Review — 2019

  In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.

  https://www.kdnuggets.com/2020/01/nlp-year-review-2019.html

• An Introductory Guide to NLP for Data Scientists with 7 Common Techniques">An Introductory Guide to NLP for Data Scientists with 7 Common Techniques

  Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.

  https://www.kdnuggets.com/2020/01/intro-guide-nlp-data-scientists.html

• Automatic Text Summarization in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.

  https://www.kdnuggets.com/2019/12/automatic-text-summarization-nutshell.html

• 5 Techniques to Prevent Overfitting in Neural Networks

  In this article, I will present five techniques to prevent overfitting while training neural networks.

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html

• Spark NLP 101: LightPipeline

  A Pipeline is specified as a sequence of stages, and each stage is either a Transformer or an Estimator. These stages are run in order, and the input DataFrame is transformed as it passes through each stage. Now let’s see how this can be done in Spark NLP using Annotators and Transformers.

  https://www.kdnuggets.com/2019/11/spark-nlp-101-lightpipeline.html

• Text Encoding: A Review

  We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.

  https://www.kdnuggets.com/2019/11/text-encoding-review.html

• Three Methods of Data Pre-Processing for Text Classification

  This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

  https://www.kdnuggets.com/2019/11/ibm-data-preprocessing-text-classification.html

• A 2019 Guide for Automatic Speech Recognition

  In this article, we’ll look at a couple of papers aimed at solving the problem of automated speech recognition with machine and deep learning.

  https://www.kdnuggets.com/2019/09/2019-guide-automatic-speech-recognition.html

• Introducing IceCAPS: Microsoft’s Framework for Advanced Conversation Modeling

  The new open source framework that brings multi-task learning to conversational agents.

  https://www.kdnuggets.com/2019/09/introducing-icecaps-microsofts-framework-advanced-conversation-modeling.html

• Deep Learning Next Step: Transformers and Attention Mechanism">Deep Learning Next Step: Transformers and Attention Mechanism

  With the pervasive importance of NLP in so many of today's applications of deep learning, find out how advanced translation techniques can be further enhanced by transformers and attention mechanisms.

  https://www.kdnuggets.com/2019/08/deep-learning-transformers-attention-mechanism.html

• Deep Learning for NLP: Creating a Chatbot with Keras!">Deep Learning for NLP: Creating a Chatbot with Keras!

  Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-creating-chatbot-keras.html

• A Gentle Introduction to Noise Contrastive Estimation

  Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.

  https://www.kdnuggets.com/2019/07/introduction-noise-contrastive-estimation.html

• Neural Code Search: How Facebook Uses Neural Networks to Help Developers Search for Code Snippets

  Developers are always searching for answers to questions about their code. But how do they ask the right questions? Facebook is creating new NLP neural networks to help search code repositories that may advance information retrieval algorithms.

  https://www.kdnuggets.com/2019/07/neural-code-facebook-uses-neural-networks.html

• Examining the Transformer Architecture: The OpenAI GPT-2 Controversy 

  GPT-2 is a generative model, created by OpenAI, trained on 40GB of Internet to predict the next word. And OpenAI found this model to be SO good that they did not release the fully trained model  due to their concerns about malicious applications of the technology.

  https://www.kdnuggets.com/2019/06/transformer-openai-gpt2.html

• All you need to know about text preprocessing for NLP and Machine Learning

  We present a comprehensive introduction to text preprocessing, covering the different techniques including stemming, lemmatization, noise removal, normalization, with examples and explanations into when you should use each of them.

  https://www.kdnuggets.com/2019/04/text-preprocessing-nlp-machine-learning.html

• Building a Recommender System

  A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.

  https://www.kdnuggets.com/2019/04/building-recommender-system.html

• Deconstructing BERT, Part 2: Visualizing the Inner Workings of Attention

  In this post, the author shows how BERT can mimic a Bag-of-Words model. The visualization tool from Part 1 is extended to probe deeper into the mind of BERT, to expose the neurons that give BERT its shape-shifting superpowers.

  https://www.kdnuggets.com/2019/03/deconstructing-bert-part-2-visualizing-inner-workings-attention.html

• Deconstructing BERT: Distilling 6 Patterns from 100 Million Parameters

  Google’s BERT algorithm has emerged as a sort of “one model to rule them all.” BERT builds on two key ideas that have been responsible for many of the recent advances in NLP: (1) the transformer architecture and (2) unsupervised pre-training.

  https://www.kdnuggets.com/2019/02/deconstructing-bert-distilling-patterns-100-million-parameters.html

• Are BERT Features InterBERTible?

  This is a short analysis of the interpretability of BERT contextual word representations. Does BERT learn a semantic vector representation like Word2Vec?

  https://www.kdnuggets.com/2019/02/bert-features-interbertible.html

• A Quick Guide to Feature Engineering

  Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.

  https://www.kdnuggets.com/2019/02/quick-guide-feature-engineering.html

• ELMo: Contextual Language Embedding

  Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.

  https://www.kdnuggets.com/2019/01/elmo-contextual-language-embedding.html

• Building an image search service from scratch

  By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.

  https://www.kdnuggets.com/2019/01/building-image-search-service-from-scratch.html

• 10 Exciting Ideas of 2018 in NLP

  We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.

  https://www.kdnuggets.com/2019/01/10-exciting-ideas-2018-nlp.html

• NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing">NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing

  Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.

  https://www.kdnuggets.com/2019/01/nlp-overview-modern-deep-learning-techniques.html

• BERT: State of the Art NLP Model, Explained

  BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.

  https://www.kdnuggets.com/2018/12/bert-sota-nlp-model-explained.html

• Machine Learning & AI Main Developments in 2018 and Key Trends for 2019">Machine Learning & AI Main Developments in 2018 and Key Trends for 2019

  As we bid farewell to one year and look to ring in another, KDnuggets has solicited opinions from numerous Machine Learning and AI experts as to the most important developments of 2018 and their 2019 key trend predictions.

  https://www.kdnuggets.com/2018/12/predictions-machine-learning-ai-2019.html

• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more

  A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.

  https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html

• Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies

  The aim of this article is to give you a good understanding of existing, traditional model interpretation methods, their limitations and challenges. We will also cover the classic model accuracy vs. model interpretability trade-off and finally take a look at the major strategies for model interpretation.

  https://www.kdnuggets.com/2018/12/explainable-ai-model-interpretation-strategies.html

• Multi-Class Text Classification with Doc2Vec & Logistic Regression

  Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.

  https://www.kdnuggets.com/2018/11/multi-class-text-classification-doc2vec-logistic-regression.html

• Text Preprocessing in Python: Steps, Tools, and Examples

  We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.

  https://www.kdnuggets.com/2018/11/text-preprocessing-python.html

• The Main Approaches to Natural Language Processing Tasks">The Main Approaches to Natural Language Processing Tasks

  Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.

  https://www.kdnuggets.com/2018/10/main-approaches-natural-language-processing-tasks.html

• More Effective Transfer Learning for NLP

  Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.

  https://www.kdnuggets.com/2018/10/more-effective-transfer-learning-nlp.html

• Deep Learning for NLP: An Overview of Recent Trends">Deep Learning for NLP: An Overview of Recent Trends

  A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.

  https://www.kdnuggets.com/2018/09/deep-learning-nlp-overview-recent-trends.html

• Topic Modeling with LSA, PLSA, LDA & lda2Vec">Topic Modeling with LSA, PLSA, LDA & lda2Vec

  This article is a comprehensive overview of Topic Modeling and its associated techniques.

  https://www.kdnuggets.com/2018/08/topic-modeling-lsa-plsa-lda-lda2vec.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• Detecting Sarcasm with Deep Convolutional Neural Networks">Detecting Sarcasm with Deep Convolutional Neural Networks

  Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.

  https://www.kdnuggets.com/2018/06/detecting-sarcasm-deep-convolutional-neural-networks.html

• Natural Language Processing Nuggets: Getting Started with NLP

  Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.

  https://www.kdnuggets.com/2018/06/getting-started-natural-language-processing.html

• How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

  An end-to-end example of how to build a system that can search objects semantically.

  https://www.kdnuggets.com/2018/06/natural-language-semantic-search-arbitrary-objects-deep-learning.html

• 5 Machine Learning Projects You Should Not Overlook, June 2018">5 Machine Learning Projects You Should Not Overlook, June 2018

  Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!

  https://www.kdnuggets.com/2018/06/5-machine-learning-projects-overlook-jun-2018.html

• An Introduction to Deep Learning for Tabular Data

  This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.

  https://www.kdnuggets.com/2018/05/introduction-deep-learning-tabular-data.html

• Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText

  Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.

  https://www.kdnuggets.com/2018/05/implementing-deep-learning-methods-feature-engineering-text-data-fasttext.html

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model

  The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.

  https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-glove.html

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model

  Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.

  https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-skip-gram.html

• Understanding Feature Engineering: Deep Learning Methods for Text Data

  Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.

  https://www.kdnuggets.com/2018/03/understanding-feature-engineering-deep-learning-methods-text-data.html

• Natural Language Processing Library for Apache Spark – free to use

  Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.

  https://www.kdnuggets.com/2017/11/natural-language-processing-library-apache-spark.html

• 7 Types of Artificial Neural Networks for Natural Language Processing">7 Types of Artificial Neural Networks for Natural Language Processing

  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

• Putting the “Science” Back in Data Science">Putting the “Science” Back in Data Science

  The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.

  https://www.kdnuggets.com/2017/09/science-data-science.html

• How I Used Deep Learning To Train A Chatbot To Talk Like Me">How I Used Deep Learning To Train A Chatbot To Talk Like Me

  In this post, we’ll be looking at how we can use a deep learning model to train a chatbot on my past social media conversations in hope of getting the chatbot to respond to messages the way that I would.

  https://www.kdnuggets.com/2017/08/deep-learning-train-chatbot-talk-like-me.html

• Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions

  We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.

  https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.html

• Cartoon: the distance between Espresso and Cappuccino

  This cartoon takes a vector space approach to your favorite drinks and examines the distance between Espresso and Cappuccino. Warning: this is only funny to Data Scientists and mathematicians.

  https://www.kdnuggets.com/2017/04/cartoon-word2vec-espresso-cappuccino.html

• Questions To Ask When Moving Machine Learning From Practice to Production

  An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.

  https://www.kdnuggets.com/2016/11/moving-machine-learning-practice-production.html

• Introduction to Local Interpretable Model-Agnostic Explanations (LIME)

  Learn about LIME, a technique to explain the predictions of any machine learning classifier.

  https://www.kdnuggets.com/2016/08/introduction-local-interpretable-model-agnostic-explanations-lime.html

• 7 Steps to Understanding Deep Learning

  There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!

  https://www.kdnuggets.com/2016/01/seven-steps-deep-learning.html

• Deep Learning for Visual Question Answering

  Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.

  https://www.kdnuggets.com/2015/11/deep-learning-visual-question-answering.html

• Understanding Convolutional Neural Networks for NLP

  Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.

  https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

• Facebook Open Sources deep-learning modules for Torch

  We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.

  https://www.kdnuggets.com/2015/02/facebook-open-source-deep-learning-torch.html

• Vector Databases in AI and LLM Use Cases

  Learn about Vectors and How Storing Data Can Be Used in LLM Applications.

  https://www.kdnuggets.com/vector-databases-in-ai-and-llm-use-cases

• Vector Database for LLMs, Generative AI, and Deep Learning

  Exploring the limitless possibilities of AI and making it context-aware.

  https://www.kdnuggets.com/vector-database-for-llms-generative-ai-and-deep-learning

• 4 Steps to Become a Generative AI Developer

  In this post, we will cover what a generative AI developer does, what tools you need to master, and how to get started.

  https://www.kdnuggets.com/4-steps-to-become-a-generative-ai-developer

• How to Access and Use Gemini API for Free

  Learn how to integrate advanced AI multimodal models into your project using a simple Python API.

  https://www.kdnuggets.com/how-to-access-and-use-gemini-api-for-free

• How to Make Large Language Models Play Nice with Your Software Using LangChain

  Beyond simply chatting with an AI model and how LangChain elevates LLM interactions with humans.

  https://www.kdnuggets.com/how-to-make-large-language-models-play-nice-with-your-software-using-langchain

• An Honest Comparison of Open Source Vector Databases

  We will explore their use cases, key features, performance metrics, supported programming languages, and more to provide a comprehensive and unbiased overview of each database.

  https://www.kdnuggets.com/an-honest-comparison-of-open-source-vector-databases

• Maximize Performance in Edge AI Applications

  This article provides an overview of the strategies for optimizing AI system performance in edge AI deployments.

  https://www.kdnuggets.com/maximize-performance-in-edge-ai-applications

• A Comprehensive Guide to Pinecone Vector Databases

  This blog discusses vector databases, specifically pinecone vector databases. A vector database is a type of database that stores data as mathematical vectors, which represent features or attributes. These vectors have multiple dimensions, capturing complex data relationships. This allows for efficient similarity and distance calculations, making it useful for tasks like machine learning, data analysis, and recommendation systems.

  https://www.kdnuggets.com/a-comprehensive-guide-to-pinecone-vector-databases