Unite.AI

AUGUST 1, 2023

GPT-4: Prompt Engineering ChatGPT has transformed the chatbot landscape, offering human-like responses to user inputs and expanding its applications across domains – from software development and testing to business communication, and even the creation of poetry. Prompt 1 : “Tell me about Convolutional Neural Networks.”

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Pickl AI

JANUARY 26, 2025

Convolutional Neural Networks (CNNs) Convolutional Neural Networks ( CNNs ) are specialised Deep Learning models that process and analyse visual data. Transformers are the foundation of many state-of-the-art architectures, such as BERT and GPT.

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AssemblyAI

SEPTEMBER 22, 2023

Long-term coherence (semantic modeling) tokens :  A second component based on w2v-BERT , generates 25 semantic tokens per second that represent features of large-scale composition , such as motifs, or consistency in the timbres. It was pre-trained to generate masked tokens in speech and fine-tuned on 8,200 hours of music.

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Marktechpost

JANUARY 15, 2025

Technical Details and Benefits Deep learning relies on artificial neural networks composed of layers of interconnected nodes. Notable architectures include: Convolutional Neural Networks (CNNs): Designed for image and video data, CNNs detect spatial patterns through convolutional operations.

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Topbots

OCTOBER 4, 2023

Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g., Masking in BERT architecture ( illustration by Misha Laskin ) Another common type of generative AI model are diffusion models for image and video generation and editing.

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Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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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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