IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

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Convolutional Neural Networks Data Mining Categorization Machine Learning 134

Becoming Human

MARCH 16, 2023

Photo by NASA on Unsplash Hello and welcome to this post, in which I will study a relatively new field in deep learning involving graphs — a very important and widely used data structure. This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications.

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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool.

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Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. . Convolutional Neural Networks (CNNs) CNNs are integral to the image encoder of the Segment Anything Model architecture.

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Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., a social media post or product description).

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Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

Heartbeat

FEBRUARY 8, 2024

Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

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Viso.ai

DECEMBER 20, 2023

Computer Vision and Deep Learning in Smart Cities – Build with Viso Suite Supervised learning versus unsupervised learning: Key differences In the following, we will discuss the differences between supervision vs. unsupervised learning.

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The MLOps Blog

FEBRUARY 22, 2024

Continual learning is a set of approaches to train machine learning models incrementally, using data samples only once as they arrive. Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks.

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