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. This capability makes them particularly effective for tasks such as image and speech recognition, where traditional algorithms may struggle.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Examples include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), Claude, etc. with labeled data.

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Topbots

OCTOBER 4, 2023

Generated with Bing and edited with Photoshop Predictive AI has been driving companies’ ROI for decades through advanced recommendation algorithms, risk assessment models, and fraud detection tools. The predictive AI algorithms can be used to predict a wide range of variables, including continuous variables (e.g.,

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. 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. This is embedding/vector/vector embedding for this article. using its Spectrogram ).

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Viso.ai

JULY 9, 2024

But, there are open source models like German-BERT that are already trained on huge data corpora, with many parameters. Through transfer learning, representation learning of German-BERT is utilized and additional subtitle data is provided. Some common free-to-use pre-trained models include BERT, ResNet , YOLO etc.

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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. 4: Algorithmic Trading and Market Analysis No.5: 4: Algorithmic Trading and Market Analysis No.5: In this article, we present 7 key applications of computer vision in finance: No.1:

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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. Popular image recognition algorithms include ResNet , VGG , YOLOv3 , and YOLOv7.

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Lexalytics

APRIL 5, 2021

This would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ]. Note that Geoff Hinton was a co-author on this paper: his interest in neural networks was finally vindicated. The figure above shows a back-propagation network.

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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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Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

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Viso.ai

DECEMBER 18, 2023

ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. ONNX (Open Neural Network Exchange) is an open-source format. A popular library for traditional machine learning algorithms.

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Heartbeat

SEPTEMBER 20, 2023

This technology has broad applications, including aiding individuals with visual impairments, improving image search algorithms, and integrating optical recognition with advanced language generation to enhance human-machine interactions. Various algorithms are employed in image captioning, including: 1.

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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. Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval.

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Heartbeat

OCTOBER 12, 2023

One of the standout achievements in this domain is the development of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

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ML Review

DECEMBER 16, 2019

Source: HyperE Traditional embedding algorithms place the learned vectors in Euclidean “flat” space of possibly high dimensionality (50–200) where distance between two vectors can be expressed with Euclidean geometry. In contrast, hyperbolic algorithms employ Poincare balls and hyperbolic space.

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Marek Rei

NOVEMBER 12, 2019

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks Awni Y. link] Experiments with a genetic algorithm for training neural networks to play Atari games.

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Sebastian Ruder

JANUARY 19, 2021

A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al.,  While Transformers have achieved large success in NLP, they were—up until recently—less successful in computer vision where convolutional neural networks (CNNs) still reigned supreme.

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Unite.AI

AUGUST 8, 2023

These algorithms take input data, such as a text or an image, and pair it with a target output, like a word translation or medical diagnosis. Information Retrieval: Using LLMs, such as BERT or GPT, as part of larger architectures to develop systems that can fetch and categorize information. They're about mapping and prediction.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

Use Case Model Name Size On Disk Number of Parameters CV resnet50 100Mb 25M CV convnext_base 352Mb 88M CV vit_large_patch16_224 1.2Gb 304M NLP bert-base-uncased 436Mb 109M NLP roberta-large 1.3Gb 335M The following table lists the GPU instances tested. The impact is more for models using a convolutional neural network (CNN).

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NLP People

JULY 24, 2019

As the following chart shows, research activity has been flourishing in the past years: Figure 1: Paper quantity published at the ACL conference by years In the following, we summarize some core trends in terms of data strategies, algorithms, tasks as well as multilingual NLP. Neural Networks are the workhorse of Deep Learning (cf.

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John Snow Labs

MAY 8, 2024

Nevertheless, the trajectory shifted remarkably with the introduction of advanced architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), including subsequent versions such as OpenAI’s GPT-3. A notable study by Esteva et al.

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