KDnuggets

NOVEMBER 10, 2021

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. A machine can only understand numbers.

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Data Science Dojo

AUGUST 18, 2023

Embeddings are vectors that represent the meaning of words or phrases. ChatGPT is also capable of answering questions, providing summaries of factual topics, and generating different creative text formats. Using keywords helps the LLM focus on the right topic, and providing examples helps the LLM learn what you are looking for.

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Data Science Dojo

JANUARY 2, 2024

Embedding-based methods : Methods like BERTScore use embeddings (vector representations of text) to evaluate semantic similarity between the model’s output and reference texts. Lower perplexity values indicate better performance.

Natural Language Processing 332

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IBM Data Science in Practice

NOVEMBER 11, 2022

Using the topic modeling approach, a machine can sift through unlimited lists of unstructured content into similar documents. So everyone wants the best Topic Modeling tool which they can use directly with their unstructured data & got the best results. These pre-defined topics are fixed.

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Mlearning.ai

APRIL 1, 2023

Word embeddings are vector representations of words in a high-dimensional space that capture the semantics and context of words. The embedding layer is the layer that learns the vector representation of the words. The vector representation of the centre word is the output of the hidden layer. CBOW Architecture. DM Architecture.

Deep Learning 52

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Data Science Dojo

FEBRUARY 15, 2023

Google, checklists, algorithms and data structures, and project-management Incidentally, a Google search using search terms related to the topic at hand suggests a consensus vis-a-vis best practices for solving problems, and/or simplifying our lives. To the rescue (!):

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Hacker News

NOVEMBER 15, 2023

However, it does not serve users who want a deeper dive into a current or more specific topic. That’s when researchers in information retrieval prototyped what they called question-answering systems, apps that use natural language processing ( NLP ) to access text, initially in narrow topics such as baseball.

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Towards AI

FEBRUARY 23, 2024

As you wander through the shelves, you notice that some books share similar themes or topics. Unlike classification algorithms, such as Random Forest or Support Vector Machines, whose training relies on labeled data points, clustering algorithms work on unlabeled data, aiming to discover the structures and patterns inside the dataset.

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Mlearning.ai

JUNE 23, 2023

Comprehension of Complex Topics : LLMs might struggle to comprehend highly specialized or technical subjects that require deep domain knowledge. A new database — Vector Database A vector DB indexes and searches vector embeddings for faster and similarity search.

Database 52

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Mlearning.ai

MAY 12, 2023

Support Vector Machine: A Comprehensive Guide — Part1 Support Vector Machines (SVMs) are a type of supervised learning algorithm used for classification and regression analysis. Source: Alteryx To explain SVM I have divided this topic into 10 subtopics. What is SVM? Equation of a Line. Distance of a point from a plane.

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Data Science Dojo

NOVEMBER 3, 2023

Subscribe now and start learning.   Link to channel AssemblyAI Learn about Llama index, vector databases and LangChain, explore how to build your own coding assistant with ChatGPT, and apply large language models to audio data with AssemblyAI. This channel offers plentiful learning tutorials within the domain of large language models.

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Data Science Dojo

NOVEMBER 3, 2023

Subscribe now and start learning.   Link to channel AssemblyAI Learn about Llama index, vector databases and LangChain, explore how to build your own coding assistant with ChatGPT, and apply large language models to audio data with AssemblyAI. This channel offers plentiful learning tutorials within the domain of large language models.

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Mlearning.ai

JANUARY 3, 2024

Even Goldberg and Levy published another article just on that topic a year after word2vec was released. If we choose a vector for each symbol, we can use the so-called one-hot vector being “1” for each element in the vector. For 20,000 words, the vector has 20,000 dimensions so it’s very heavy to compute.

Deep Learning 52

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ODSC - Open Data Science

NOVEMBER 2, 2023

Building Production-Grade Vector Search By enabling quick and structured access to data representations, vector stores empower machine learning models to process and learn from complex data with remarkable efficiency.

Data Science 52

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Data Science Dojo

NOVEMBER 3, 2023

Subscribe now and start learning.   Link to channel AssemblyAI Learn about Llama index, vector databases and LangChain, explore how to build your own coding assistant with ChatGPT, and apply large language models to audio data with AssemblyAI. This channel offers plentiful learning tutorials within the domain of large language models.

Natural Language Processing 195

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ODSC - Open Data Science

AUGUST 17, 2023

The blog mentions that AdaTape uses a vector representation to represent each input to select a variable-sized sequence of tape tokens dynamically. It’s a more general method for generating the tape bank by using a set of trainable vectors as tape tokens. It also uses an adaptive tape reading mechanism.

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Pickl AI

AUGUST 1, 2023

Topic Modeling With text mining, it is possible to identify and categorize topics and themes within large collections of documents. Text Vectorization Techniques Text vectorization is a crucial step in text mining, where text data is transformed into numerical representations that can be processed by Machine Learning algorithms.

Data Analysis 52

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Mlearning.ai

AUGUST 22, 2023

This topic has been the subject of many papers. Embeddings map items of unstructured data to high-dimensional real vectors. Word embeddings are vector representations of words, where each dimension represents a different feature or aspect of the word’s meaning. Tokens are common sequences of characters found in text.

AI 52

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PyImageSearch

FEBRUARY 20, 2023

Next, we find the function’s gradient at a particular point (scalar) and for a list of points (vector). Next, let us see how to do the same with vectors. What happens if we send the entire vector to our gradient function? . What happens if we send the entire vector to our gradient function?

Machine Learning 114

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Heartbeat

NOVEMBER 7, 2023

NLP — Topic Modeling For a TV Series Episode Summary Introduction Many of us watch TV shows for leisure within our daily mundane routines via various online streaming platforms (such as Amazon Prime or Netflix), each having its share of show categories. Dirichlet indicates that the model assumes the distribution of topics in a document.

Machine Learning 40

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AWS Machine Learning Blog

MARCH 1, 2024

Context is retrieved from the vector database based on the user query. Use cases for hybrid search The following are some common use cases for hybrid search: Open domain question answering – This involves answering questions on a wide variety of topics. Hybrid search can better handle such open-ended dialogs.

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Towards AI

JULY 19, 2023

There are many Vectorization techniques:- Bag of Words (BOW) TFIDF Word Embeddings This is a topic that will require a whole article, so I will cover this in the next article.

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ODSC - Open Data Science

MARCH 8, 2024

Definition Embedding is a process of transforming data into numerical vectors and it is used to represent text, images, audio, or other complex data types in a multi-dimensional space that preserves the semantic similarity and relevance of the original data. It is an open-source vector database designed for efficient similarity search.

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Mlearning.ai

MAY 3, 2023

Vector Databases — Long Term Memory for AI Photo by Sven Brandsma on Unsplash Introduction In natural language processing (NLP), a vector is a mathematical representation of a word or text document. In NLP, a vector can be generated using techniques such as word embedding or document embedding. toarray()) [[0.16387439 0.

Database 52

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Towards AI

JUNE 21, 2023

LangChain, a potent tool for working with LLMs, coupled with Deep Lake, the vector database for all AI data that provides serverless memory to LLMs, forms the course’s centerpiece. Get started with the LangChain & Vector Databases in Production course today, free of charge. The future is right here, right now.

Database 87

Database AI AI Artificial Intelligence 87

IBM Journey to AI blog

OCTOBER 18, 2023

Research Scientists Berthold Reinwald and Nasrullah Sheikh will be sharing more about their Presto Connector for vector search on data indexed by vector databases. Created for beginners to advanced Presto users, there will be three workshops on different Presto topics.

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ODSC - Open Data Science

FEBRUARY 13, 2023

A few standout topics include model deployment and inferencing, MLOps, and multi-cloud machine learning. Machine Learning for Beginners Learn the essentials of machine learning including how Support Vector Machines, Naive Bayesian Classifiers, and Upper Confidence Bound algorithms work.

Machine Learning 98

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AWS Machine Learning Blog

FEBRUARY 2, 2024

Embeddings are just vectors of floating point numbers, so we can analyze them to help answer three important questions: Is our reference data changing over time? In this post, you’ll learn about some of the considerations for embedding vector analysis and detecting signals of embedding drift.

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Towards AI

FEBRUARY 13, 2023

Last Updated on February 13, 2023 by Editorial Team What happened this week in AI by Louis ChatGPT and generative AI are still hot topics this week. ChatGPT has been a topic of discussion since its launch, with opinions divided between its potential benefits and perceived threats. Hottest News 1.Is Is ChatGPT Coming for Your Job?

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ODSC - Open Data Science

FEBRUARY 22, 2024

Troubleshooting Large Language Models with Amber Roberts This podcast with Amber Roberts of Arize AI examines recent research on ways to measure vector/embedding drift for image and language models, and the insights gained from testing different approaches. We’ll also touch upon details of their book and what went into writing it.

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Towards AI

APRIL 5, 2023

The articles cover a range of topics, from the basics of Rust to more advanced machine learning concepts, and provide practical examples to help readers get started with implementing ML algorithms in Rust.

Machine Learning 95

Machine Learning Machine Learning Support Vector Machines Clustering 95

Data Science Dojo

SEPTEMBER 11, 2023

LLM parameters They use vectors to represent words numerically, allowing them to understand and generate text. Temperature is set lower to provide precise, on-topic answers, and Top-k ensures the best token selection for each response (temperature, Top-k).

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Dataconomy

OCTOBER 13, 2023

He shares diverse insights as he gears up to enlighten the Global DevSlam audience—from his personal aspirations and cultural immersion at the event to in-depth musings on trending industry topics like Cloud Native and Serverless Applications. I’m passionate about this AI ecosystem, and I want to make the biggest impact I can.

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AWS Machine Learning Blog

NOVEMBER 7, 2023

Large language models (LLMs) with their broad knowledge, can generate human-like text on almost any topic. This synergy enables more accurate topic modeling, content recommendation, and semantic search capabilities. Embedding models, a branch of ML, transform complex data, such as words or phrases, into continuous vector spaces.

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Mlearning.ai

JULY 2, 2023

Source: Scaler Topics Model Architecture: The core of the Transformer model lies in its unique architecture. The input sequence is first transformed into embedding vectors, augmented with positional information using learned positional encodings. These vectors are derived from linear transformations of the input embeddings.

Natural Language Processing 52

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Data Science Dojo

DECEMBER 14, 2023

These blogs stand out not just for their depth of content but also for their ability to make complex topics accessible to a broader audience. Embeddings are continuous vector representations of words or tokens that capture their semantic meanings in a high-dimensional space.

Data Science 195

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Mlearning.ai

JANUARY 26, 2023

Introduction Natural language processing (NLP) sentiment analysis is a powerful tool for understanding people’s opinions and feelings toward specific topics. It uses text-based data to analyze how people feel about a particular topic and can help businesses to understand the broad sentiment of their customer base.

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