Remove Convolutional Neural Networks Remove Metadata Remove Neural Network
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Python Speech Recognition in 2025

AssemblyAI

Unlike many natural language processing (NLP) models, which were historically dominated by recurrent neural networks (RNNs) and, more recently, transformers, wav2letter is designed entirely using convolutional neural networks (CNNs). What sets wav2letter apart is its unique architecture.

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Read graphs, diagrams, tables, and scanned pages using multimodal prompts in Amazon Bedrock

AWS Machine Learning Blog

Name a product and extract metadata to generate a tagline and description In the field of marketing and product development, coming up with a perfect product name and creative promotional content can be challenging. The image was generated using the Stability AI (SDXL 1.0) model on Amazon Bedrock.

LLM 106
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Demand forecasting at Getir built with Amazon Forecast

AWS Machine Learning Blog

Algorithm Selection Amazon Forecast has six built-in algorithms ( ARIMA , ETS , NPTS , Prophet , DeepAR+ , CNN-QR ), which are clustered into two groups: statististical and deep/neural network. Deep/neural network algorithms also perform very well on sparse data set and in cold-start (new item introduction) scenarios.

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What AI Music Generators Can Do (And How They Do It)

AssemblyAI

The model is trained conditionally on text metadata alongside audio file duration and initiation time. As for any diffusion model , Stable Audio adds noise to the audio vector, which a U-Net Convolutional Neural Network learns to remove, guided by the text and timing embeddings.

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SEER: A Breakthrough in Self-Supervised Computer Vision Models?

Unite.AI

However, this approach needs to filter images, and it works best only when a textual metadata is present. The figure below compares the pre-training of a ResNetXt101-32dx8d architecture trained on random images with the same architecture being trained on labeled images with hashtags and metadata, and reports the top-1 accuracy for both.

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Google AI Researchers Investigate Temporal Distribution Shifts in Deep Learning Models for CTG Analysis

Marktechpost

In response, Google utilizes a deep neural network, CTG-net, to process the time-series data of fetal heart rate (FHR) and uterine contractions (UC) in order to predict fetal hypoxia. The CTG-net model utilizes a convolutional neural network (CNN) architecture to analyze FHR and UC signals, learning their temporal relationships.

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Using Machine Learning for Sentiment Analysis: a Deep Dive

DataRobot Blog

The Amazon Product Reviews Dataset provides over 142 million Amazon product reviews with their associated metadata, allowing machine learning practitioners to train sentiment models using product ratings as a proxy for the sentiment label. Because these networks are recurrent, they are ideal for working with sequential data such as text.