Explosion

NOVEMBER 9, 2016

Most NLP problems can be reduced to machine learning problems that take one or more texts as input. However, most NLP problems require understanding of longer spans of text, not just individual words. This has always been a huge weakness of NLP models. 2016) model and a convolutional neural network (CNN).

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John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

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Viso.ai

OCTOBER 10, 2024

The identification of regularities in data can then be used to make predictions, categorize information, and improve decision-making processes. While explorative pattern recognition aims to identify data patterns in general, descriptive pattern recognition starts by categorizing the detected patterns.

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Heartbeat

MARCH 8, 2023

Heartbeat these past few weeks has had lots of great articles covering the latest research, NLP use-cases, and Comet tutorials. Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates.

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Pickl AI

DECEMBER 10, 2024

Categorical Features (Nominal vs. Ordinal) Categorical features group data into distinct categories or classes, often representing qualitative attributes. Handling categorical data appropriately is essential for ensuring accurate interpretations by Machine Learning models.

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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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Viso.ai

DECEMBER 20, 2023

However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks ) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure.

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Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

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Pickl AI

NOVEMBER 20, 2024

Example In Natural Language Processing (NLP), word embeddings are often represented as vectors. Common preprocessing steps include normalization (scaling features), encoding categorical variables (one-hot encoding), and handling missing values using imputation techniques that often rely on matrix operations.

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ). Vaswani et al.

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Heartbeat

AUGUST 29, 2023

Types of Deep Learning Approaches A variety of methods and designs are used to train neural networks under the umbrella of deep learning. Some of the symbolic approaches of deep learning are listed below: CNNs (Convolutional Neural Networks) : CNNs are frequently employed in image and video recognition jobs.

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Viso.ai

JULY 9, 2024

General categorization and approaches of Transfer Learning – Source The vehicle categories could be ‘Sedan’, ‘SUV’, ‘Truck’, ”Two-wheeler’, ‘Commercial trucks’, etc. VGG16 has a CNN ( Convolutional Neural Network ) based architecture that has 16 layers.

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Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Then, compile the model, harnessing the power of the Adam optimizer and categorical cross-entropy loss.

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Viso.ai

JUNE 24, 2024

We can categorize the types of AI for the blind and their functions. These models usually use a classification algorithm like a Convolutional Neural Network (CNN) or a multimodal architecture. Thus, the software will need to have various features like object detection, image captioning, navigation, and more.

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The MLOps Blog

FEBRUARY 22, 2024

Methods for continual learning can be categorized as regularization-based, architectural, and memory-based, each with specific advantages and drawbacks. This approach is widespread in NLP, where one model might learn to perform text classification, named entity recognition, and text summarization.

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Unite.AI

AUGUST 8, 2023

Instead of complex and sequential architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context. How Are LLMs Used?

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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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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). Airbnb uses ViTs for several purposes in their photo tour feature: Image classification : Categorizing photos into different room types (bedroom, bathroom, kitchen, etc.)

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