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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Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs).

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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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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

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Pickl AI

AUGUST 14, 2024

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g., Data moves in one direction—from input to output.

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Mlearning.ai

MAY 11, 2023

Indeed, this AI is a powerful natural language processing tool that can be used to generate human-like language, making it an ideal tool for creating chatbots, virtual assistants, and other applications that require natural language interactions. What lies behind GPT is a type of artificial intelligence (AI) called a neural network.

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Mlearning.ai

MAY 30, 2023

As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. Systems for machine translation, automatic text completion, and chatbots can all use this skill. We will go into the world of RNNs in this blog and examine their construction, uses, and drawbacks.

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Snorkel AI

MARCH 1, 2023

Chatbots and virtual assistants ChatGPT demonstrated that foundation models can serve as the seed for competent chat bots and virtual assistants that may help businesses provide customer support and answer common questions. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. Technologies such as Optical Character Recognition (OCR) and Natural Language Processing (NLP) are foundational to this. 1: Fraud Detection and Prevention No.2:

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Viso.ai

JULY 9, 2024

We can choose a language model like BERT that can parse human text to build an NLP model such as a text summary. VGG16 has a CNN ( Convolutional Neural Network ) based architecture that has 16 layers. The time taken to develop and present these chatbots to market has reduced with transfer learning.

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Pragnakalp

JANUARY 1, 2024

Compared with traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), transformers differ in their ability to capture long-range dependencies and contextual information. They’re used widely in neural machine translation (NMT).

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Pickl AI

SEPTEMBER 25, 2024

Natural Language Processing (NLP) NLP applications powered by Deep Learning have transformed how machines understand human language. Chatbots and Virtual Assistants These AI-driven tools utilise Deep Learning to provide customer support through natural language conversations.

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Pickl AI

NOVEMBER 13, 2024

Challenges include vanishing and exploding gradients, especially in Deep Networks. Backpropagation powers applications in image recognition, NLP, and autonomous systems. Understanding Backpropagation Backpropagation, short for “backward propagation of errors,” is a core algorithm for training artificial neural networks.

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Pickl AI

SEPTEMBER 18, 2023

PixelRNN models generate pixels sequentially, while PixelCNN models use a convolutional neural network to model the conditional distribution of each pixel. Text Generation: GPT-3: Generates coherent and contextually relevant text, widely used for chatbots, content generation, and text completion.

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Viso.ai

JULY 9, 2024

Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests. For example, in Natural Language Processing (NLP), the model works by predicting the next word in a sequence.

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Explosion

AUGUST 3, 2017

We’ve been working on Prodigy since we first launched Explosion last year, alongside our open-source NLP library spaCy and our consulting projects (it’s been a busy year!). The model is a convolutional neural network stacked with a unigram bag-of-words. s new text classification system (currently in alpha).

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Pickl AI

SEPTEMBER 23, 2024

In image recognition, Convolutional Neural Networks (CNNs) can accurately identify objects and faces in images. Natural Language Processing (NLP) uses Deep Learning models to understand and generate human language, enabling applications like chatbots and translation.

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Mlearning.ai

JUNE 8, 2023

Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. A few embeddings for different data type For text data, models such as Word2Vec , GLoVE , and BERT transform words, sentences, or paragraphs into vector embeddings. using its Spectrogram ).

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Sequence to Sequence Learning with Neural Networks.

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Unite.AI

AUGUST 8, 2023

These advances have fueled applications in document creation, chatbot dialogue systems, and even synthetic music composition. An essential architectural backbone for many diffusion models is the UNet —a convolutional neural network tailored for tasks requiring outputs mirroring the spatial dimension of inputs.

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Heartbeat

JANUARY 12, 2023

Other practical examples of deep learning include virtual assistants, chatbots, robotics, image restoration, NLP (Natural Language Processing), and so on. Convolution, pooling, and fully connected layers are just a few components that make up a convolutional neural network.

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

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