Search results for word embeddings

Found 204 documents, 5970 searched:

• 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

• 7 Beginner-Friendly Projects to Get You Started with ChatGPT

  And to unleash the power of AI in today’s world.

  https://www.kdnuggets.com/2023/08/7-beginnerfriendly-projects-get-started-chatgpt.html

• Brewing a Domain-Specific LLM Potion

  Make your LLM an expert in your field.

  https://www.kdnuggets.com/2023/08/brewing-domainspecific-llm-potion.html

• Breaking the Data Barrier: How Zero-Shot, One-Shot, and Few-Shot Learning are Transforming Machine Learning

  Discover the concepts of Zero-Shot, One-Shot, and Few-Shot Learning, which enable machine learning models to classify and recognize objects or patterns with a limited number of examples.

  https://www.kdnuggets.com/2023/08/breaking-data-barrier-zeroshot-oneshot-fewshot-learning-transforming-machine-learning.html

• The Drag-and-Drop UI for Building LLM Flows: Flowise AI

  Don’t have any coding experience? Don’t worry. Check out this drag-and-drop tool that helps you to build your own customized LLM flows. And guess what, you don’t have to be a tech professional!

  https://www.kdnuggets.com/2023/07/draganddrop-ui-building-llm-flows-flowise-ai.html

• More Free Courses on Large Language Models

  Interested in learning about large language models? Get up and running with these free courses from DeepLearning.AI, Google Cloud, Udacity, and more.

  https://www.kdnuggets.com/2023/06/free-courses-large-language-models.html

• What are Large Language Models and How Do They Work?

  Large language models represent a significant advancement in natural language processing and have transformed the way we interact with language-based technology. Learn why they’re important and how they work.

  https://www.kdnuggets.com/2023/05/large-language-models-work.html

• What Is ChatGPT Doing and Why Does It Work?

  In this article, we will explain how ChatGPT works and why it is able to produce coherent and diverse conversations.

  https://www.kdnuggets.com/2023/04/chatgpt-work.html

• Introducing TPU v4: Googles Cutting Edge Supercomputer for Large Language Models

  TPU v4: Google's fifth domain-specific architecture and third supercomputer for machine learning models.

  https://www.kdnuggets.com/2023/04/introducing-tpu-v4-googles-cutting-edge-supercomputer-large-language-models.html

• Introducing the Testing Library for Natural Language Processing

  Deliver reliable, safe and effective NLP models.

  https://www.kdnuggets.com/2023/04/introducing-testing-library-natural-language-processing.html

• Topic Modeling Approaches: Top2Vec vs BERTopic

  This post gives an overview of the strengths and differences of these approaches in extracting topics from text.

  https://www.kdnuggets.com/2023/01/topic-modeling-approaches-top2vec-bertopic.html

• Key Data Science, Machine Learning, AI and Analytics Developments of 2022

  It's the end of the year, and so it's time for KDnuggets to assemble a team of experts and get to the bottom of what the most important data science, machine learning, AI and analytics developments of 2022 were.

  https://www.kdnuggets.com/2022/12/key-data-science-machine-learning-ai-analytics-developments-2022.html

• Multimodal Grounded Learning with Vision and Language

  How to enable AI models to have similar capabilities: to communicate, to ground, and to learn from language.

  https://www.kdnuggets.com/2022/11/multimodal-grounded-learning-vision-language.html

• Approaches to Text Summarization: An Overview

  This article will present the main approaches to text summarization currently employed, as well as discuss some of their characteristics.

  https://www.kdnuggets.com/2019/01/approaches-text-summarization-overview.html

• Best Practices for Creating Domain-Specific AI Models

  Here are some best practices and techniques for domain-specific model adaptation that worked for us time and again.

  https://www.kdnuggets.com/2022/07/best-practices-creating-domainspecific-ai-models.html

• Classifying Long Text Documents Using BERT

  Transformer based language models such as BERT are really good at understanding the semantic context because they were designed specifically for that purpose. BERT outperforms all NLP baselines, but as we say in the scientific community, “no free lunch”. How can we use BERT to classify long text documents?

  https://www.kdnuggets.com/2022/02/classifying-long-text-documents-bert.html

• Learn Deep Learning by Building 15 Neural Network Projects in 2022

  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  https://www.kdnuggets.com/2022/01/15-neural-network-projects-build-2022.html

• The Evolution of Tokenization – Byte Pair Encoding in NLP

  Though we have SOTA algorithms for tokenization, it's always a good practice to understand the evolution trail and learning how have we reached here. Read this introduction to Byte Pair Encoding.

  https://www.kdnuggets.com/2021/10/evolution-tokenization-byte-pair-encoding-nlp.html

• Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?">Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?

  Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.

  https://www.kdnuggets.com/2021/10/trillion-parameters-gpt-3-switch-transformers-path-agi.html

• Multilabel Document Categorization, step by step example

  This detailed guide explores an unsupervised and supervised learning two-stage approach with LDA and BERT to develop a domain-specific document categorizer on unlabeled documents.

  https://www.kdnuggets.com/2021/08/multilabel-document-categorization.html

• Exploring the SwAV Method

  This post discusses the SwAV (Swapping Assignments between multiple Views of the same image) method from the paper “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments” by M. Caron et al.

  https://www.kdnuggets.com/2021/07/swav-method.html

• Semantic Search: Measuring Meaning From Jaccard to Bert

  In this article, we’ll cover a few of the most interesting — and powerful — of these techniques — focusing specifically on semantic search. We’ll learn how they work, what they’re good at, and how we can implement them ourselves.

  https://www.kdnuggets.com/2021/07/semantic-search-measuring-meaning-jaccard-bert.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3

  Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part3.html

• Fine-Tuning Transformer Model for Invoice Recognition

  The author presents a step-by-step guide from annotation to training.

  https://www.kdnuggets.com/2021/06/fine-tuning-transformer-model-invoice-recognition.html

• Multilingual CLIP with Huggingface + PyTorch Lightning

  An overview of training OpenAI's CLIP on Google Colab.

  https://www.kdnuggets.com/2021/03/multilingual-clip--huggingface-pytorch-lightning.html

• The Difficulty of Graph Anonymisation

  Lessons from network science and the difficulty of graph anonymization. A data scientist's take on the difficultly of striking a balance between privacy and utility in anonymizing connected data.

  https://www.kdnuggets.com/2021/02/difficulty-graph-anonymisation.html

• Backcasting: Building an Accurate Forecasting Model for Your Business

  This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.

  https://www.kdnuggets.com/2021/02/backcasting-building-accurate-forecasting-model-business.html

• Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality

  Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?

  https://www.kdnuggets.com/2021/02/vision-transformers-nlp-efficiency-model-generality.html

• 10 Underappreciated Python Packages for Machine Learning Practitioners">10 Underappreciated Python Packages for Machine Learning Practitioners

  Here are 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

  https://www.kdnuggets.com/2021/01/10-underappreciated-python-packages-machine-learning-practitioners.html

• How to Incorporate Tabular Data with HuggingFace Transformers

  In real-world scenarios, we often encounter data that includes text and tabular features. Leveraging the latest advances for transformers, effectively handling situations with both data structures can increase performance in your models.

  https://www.kdnuggets.com/2020/11/tabular-data-huggingface-transformers.html

• Understanding Transformers, the Data Science Way

  Read this accessible and conversational article about understanding transformers, the data science way — by asking a lot of questions that is.

  https://www.kdnuggets.com/2020/10/understanding-transformers-data-science-way.html

• 5 Fantastic Natural Language Processing Books

  This curated collection of 5 natural language processing books attempts to cover a number of different aspects of the field, balancing the practical and the theoretical. Check out these 5 fantastic selections now in order to improve your NLP skills.

  https://www.kdnuggets.com/2020/07/5-fantastic-nlp-books.html

• Building a Content-Based Book Recommendation Engine

  In this blog, we will see how we can build a simple content-based recommender system using Goodreads data.

  https://www.kdnuggets.com/2020/07/building-content-based-book-recommendation-engine.html

• PyTorch LSTM: Text Generation Tutorial

  Key element of LSTM is the ability to work with sequences and its gating mechanism.

  https://www.kdnuggets.com/2020/07/pytorch-lstm-text-generation-tutorial.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• From Languages to Information: Another Great NLP Course from Stanford">From Languages to Information: Another Great NLP Course from Stanford

  Check out another example of a Stanford NLP course and its freely available courseware.

  https://www.kdnuggets.com/2020/06/languages-information-another-great-nlp-course-stanford.html

• Google Unveils TAPAS, a BERT-Based Neural Network for Querying Tables Using Natural Language

  The new neural network extends BERT to interact with tabular datasets.

  https://www.kdnuggets.com/2020/05/google-tapas-bert-neural-network-querying-natural-language.html

• Few-Shot Image Classification with Meta-Learning

  Here is how you can teach your model to learn quickly from a few examples.

  https://www.kdnuggets.com/2020/03/few-shot-image-classification-meta-learning.html

• 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)">20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 1)

  2020 is well underway, and we bring you 20 AI, data science, and machine learning terms we should all be familiar with as the year marches onward.

  https://www.kdnuggets.com/2020/02/ai-data-science-machine-learning-key-terms-2020.html

• Inside The Machine Learning that Google Used to Build Meena: A Chatbot that Can Chat About Anything

  Meena is one of the major milestones in the history of NLU. How did Google build it?

  https://www.kdnuggets.com/2020/02/inside-machine-learning-google-build-meena-chatbot.html

• Interpretability part 3: opening the black box with LIME and SHAP

  The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.

  https://www.kdnuggets.com/2019/12/interpretability-part-3-lime-shap.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020">AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2019 and Key Trends for 2020

  As we say goodbye to one year and look forward to another, KDnuggets has once again solicited opinions from numerous research & technology experts as to the most important developments of 2019 and their 2020 key trend predictions.

  https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-research.html

• What is Machine Learning on Code?

  Not only can MLonCode help companies streamline their codebase and software delivery processes, but it also helps organizations better understand and manage their engineering talents.

  https://www.kdnuggets.com/2019/11/machine-learning-code-mloncode.html

• Research Guide for Transformers

  The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

  https://www.kdnuggets.com/2019/10/research-guide-transformers.html

• 10 Free Top Notch Natural Language Processing Courses">10 Free Top Notch Natural Language Processing Courses

  Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.

  https://www.kdnuggets.com/2019/10/10-free-top-notch-courses-natural-language-processing.html

• Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained

  The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.

  https://www.kdnuggets.com/2019/10/multi-task-learning-ernie-sota-nlp-architecture.html

• BERT, RoBERTa, DistilBERT, XLNet: Which one to use?

  Lately, varying improvements over BERT have been shown — and here I will contrast the main similarities and differences so you can choose which one to use in your research or application.

  https://www.kdnuggets.com/2019/09/bert-roberta-distilbert-xlnet-one-use.html

• My journey path from a Software Engineer to BI Specialist to a Data Scientist">My journey path from a Software Engineer to BI Specialist to a Data Scientist

  The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.

  https://www.kdnuggets.com/2019/09/journey-software-engineer-bi-data-scientist.html

• A 2019 Guide to Speech Synthesis with Deep Learning

  In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

  https://www.kdnuggets.com/2019/09/2019-guide-speech-synthesis-deep-learning.html

• Pre-training, Transformers, and Bi-directionality

  Bidirectional Encoder Representations from Transformers BERT (Devlin et al., 2018) is a language representation model that combines the power of pre-training with the bi-directionality of the Transformer’s encoder (Vaswani et al., 2017). BERT improves the state-of-the-art performance on a wide array of downstream NLP tasks with minimal additional task-specific training.

  https://www.kdnuggets.com/2019/07/pre-training-transformers-bi-directionality.html

• 10 New Things I Learnt from fast.ai Course V3

  Fastai offers some really good courses in machine learning and deep learning for programmers. I recently took their "Practical Deep Learning for Coders" course and found it really interesting. Here are my learnings from the course.

  https://www.kdnuggets.com/2019/06/things-learnt-fastai-course.html

• My favorite mind-blowing Machine Learning/AI breakthroughs">My favorite mind-blowing Machine Learning/AI breakthroughs

  We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.

  https://www.kdnuggets.com/2019/03/favorite-ml-ai-breakthroughs.html

• Beyond news contents: the role of social context for fake news detection

  Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!

  https://www.kdnuggets.com/2019/03/beyond-news-contents-role-of-social-context-for-fake-news-detection.html

• Comparing MobileNet Models in TensorFlow

  MobileNets are a family of mobile-first computer vision models for TensorFlow, designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application.

  https://www.kdnuggets.com/2019/03/comparing-mobilenet-models-tensorflow.html

• A comprehensive survey on graph neural networks

  This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.

  https://www.kdnuggets.com/2019/02/comprehensive-survey-graph-neural-networks.html

• How to solve 90% of NLP problems: a step-by-step guide">How to solve 90% of NLP problems: a step-by-step guide

  Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.

  https://www.kdnuggets.com/2019/01/solve-90-nlp-problems-step-by-step-guide.html

• Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools">Best Machine Learning Languages, Data Visualization Tools, DL Frameworks, and Big Data Tools

  We cover a variety of topics, from machine learning to deep learning, from data visualization to data tools, with comments and explanations from experts in the relevant fields.

  https://www.kdnuggets.com/2018/12/machine-learning-data-visualization-deep-learning-tools.html

• How to Engineer Your Way Out of Slow Models

  We describe how we handle performance issues with our deep learning models, including how to find subgraphs that take a lot of calculation time and how to extract these into a caching mechanism.

  https://www.kdnuggets.com/2018/11/engineer-slow-models.html

• Using Uncertainty to Interpret your Model

  We outline why you should care about uncertainty and discuss the different types, including model, data and measurement uncertainty and what different purposes these all serve.

  https://www.kdnuggets.com/2018/11/using-uncertainty-interpret-model.html

• Multi-Class Text Classification Model Comparison and Selection

  This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.

  https://www.kdnuggets.com/2018/11/multi-class-text-classification-model-comparison-selection.html

• Building a Question-Answering System from Scratch

  This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.

  https://www.kdnuggets.com/2018/10/building-question-answering-system-from-scratch.html

• Machine Reading Comprehension: Learning to Ask & Answer

  Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.

  https://www.kdnuggets.com/2018/10/machine-reading-comprehension-learning-ask-answer.html

• Financial Data Analysis – Data Processing 1: Loan Eligibility Prediction

  In this first part I show how to clean and remove unnecessary features. Data processing is very time-consuming, but better data would produce a better model.

  https://www.kdnuggets.com/2018/09/financial-data-analysis-loan-eligibility-prediction.html

• Multi-Class Text Classification with Scikit-Learn

  The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.

  https://www.kdnuggets.com/2018/08/multi-class-text-classification-scikit-learn.html

• Interpreting a data set, beginning to end

  Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.

  https://www.kdnuggets.com/2018/08/interpreting-data-set.html

• How GOAT Taught a Machine to Love Sneakers

  Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.

  https://www.kdnuggets.com/2018/08/goat-taught-machine-love-sneakers.html

• fast.ai Deep Learning Part 1 Complete Course Notes

  This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 1 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  https://www.kdnuggets.com/2018/07/fast-ai-deep-learning-part-1-notes.html

• Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health

  After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.

  https://www.kdnuggets.com/2018/06/taming-lstms-variable-sized-mini-batches-pytorch.html

• The 10 Deep Learning Methods AI Practitioners Need to Apply

  Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.

  https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.html

• Using Deep Learning to Solve Real World Problems">Using Deep Learning to Solve Real World Problems

  Do you assume that deep learning is only being used for toy problems and in self-learning scenarios? This post includes several firsthand accounts of organizations using deep neural networks to solve real world problems.

  https://www.kdnuggets.com/2017/12/using-deep-learning-solve-real-world-problems.html

• Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing

  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

• Deep Learning Reading Group: Skip-Thought Vectors

  Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.

  https://www.kdnuggets.com/2016/11/deep-learning-group-skip-thought-vectors.html

• An NLP Approach to Analyzing Twitter, Trump, and Profanity

  Who swears more? Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let’s find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets.

  https://www.kdnuggets.com/2016/11/nlp-approach-analyzing-twitter-trump-profanity.html

• Attention and Memory in Deep Learning and NLP

  An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.

  https://www.kdnuggets.com/2016/01/attention-memory-deep-learning-nlp.html