Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics.

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Towards AI

JANUARY 14, 2025

Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. The goal was to see if I could accurately identify these digits using a Transformer-based approach, which feels quite different from the traditional Convolutional Neural Network (CNN) methods I was more familiar with.

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Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data.

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. I’ll be making use of the powerful SpaCy library which makes swapping architectures in NLP pipelines a breeze.

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Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Models like GPT and BERT involve millions to billions of parameters, leading to significant processing time and energy consumption during training and inference.

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Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. The introduction of the transformer framework proved to be a milestone, facilitating the development of a new wave of language models, including OPT and BERT, which exhibit profound linguistic understanding.

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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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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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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

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Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

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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. Traditional NLP methods heavily rely on models that are trained on labeled datasets. Unlike GPT models, BERT is bidirectional. with labeled data.

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Marktechpost

DECEMBER 12, 2024

Convolutional Neural Networks (CNNs) : Convolutional Neural Networks (CNNs) are specifically designed for processing grid-like data such as images or time-series data. They utilize convolutional layers to extract spatial features by applying filters to the input data.

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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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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. The post 74 Summaries of Machine Learning and NLP Research appeared first on Marek Rei.

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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

JUNE 8, 2023

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. Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. using its Spectrogram ).

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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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DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. These embeddings are sometimes trained jointly with the model, but usually additional accuracy can be attained by using pre-trained embeddings such as Word2Vec, GloVe, BERT, or FastText.

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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

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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Lexalytics

APRIL 5, 2021

I was out of the neural net biz. Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) The base model of BERT [ 103 ] had 12 (!)

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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

DECEMBER 9, 2024

For example, in convolutional neural networks (CNNs), the lower layers detect basic features like edges and textures, while higher layers combine these features to recognise more complex patterns. By shaping how models interpret data and generalise, inductive bias influences their effectiveness in solving real-world problems.

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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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Heartbeat

SEPTEMBER 20, 2023

Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language. Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately.

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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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Deep Learning Computer Vision Neural Network Natural Language Processing 52

Marktechpost

JUNE 27, 2023

Since this is a crucial component of any deep learning or convolutional neural network system. Network convergence occurs more quickly when internal normalization is used more than external normalizing. ReLU (Rectified Linear unit) Activation Function Nowadays, the ReLU is the most popular activation function.

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ML Review

FEBRUARY 11, 2019

Understanding building blocks of ULMFIT Last week I had the time to tackle a Kaggle NLP competition: Quora Insincere Questions Classification. Tokenization is another important aspect in NLP which will not be covered here but for those who are particularly interested may visit here. In short it’s a binary classification problem.

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ML Review

DECEMBER 16, 2019

NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). Have a look ? Hands down, great work!

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Heartbeat

OCTOBER 12, 2023

These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning. They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

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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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Unite.AI

FEBRUARY 8, 2025

Models like BERT and GPT took language understanding to new depths by grasping the context of words more effectively. Transformers in Diverse Applications Beyond NLP The adaptability of transformers has extended their use well beyond natural language processing.

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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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AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. There are two notebooks provided in the repo: one for load testing CV models and another for NLP.

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NLP People

JULY 24, 2019

We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and – guess what – NLP-based analysis of ACL papers from the last 20 years. 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.

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