Pickl AI

APRIL 6, 2025

The choice of architecture is crucial because it dictates how the model processes information and learns representations from the data. Here are some of the most common and influential Deep Learning architectures: Feedforward Neural Networks (FNNs) / Multi-Layer Perceptrons (MLPs) The simplest type of ANN.

Neural Network 52

Neural Network Deep Learning Natural Language Processing Convolutional Neural Networks 52

Pickl AI

JANUARY 26, 2025

Convolutional Neural Networks (CNNs) Convolutional Neural Networks ( CNNs ) are specialised Deep Learning models that process and analyse visual data. The convolution layer applies filters (kernels) over input data, extracting essential features such as edges, textures, or shapes.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Marktechpost

APRIL 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. in 2017, marking a departure from the previous reliance on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for processing sequential data.

Large Language Models 120

Large Language Models NLP Convolutional Neural Networks Neural Network 120

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.

Convolutional Neural Networks 116

Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 116

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.

Neural Network 147

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

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.

Deep Learning 85

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 85

Unite.AI

APRIL 26, 2024

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. As mentioned earlier, the main purpose of the HR visual encoder is to offer high-resolution candidate information.

Large Language Models 162

Large Language Models Natural Language Processing Convolutional Neural Networks Neural Network 162

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! Each has a single representation for the word “well”, which combines the information for “doing well” with “wishing well”. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e.

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

Mlearning.ai

APRIL 8, 2023

Use Cases : Web Search, Information Retrieval, Text Mining Significant papers: “ Latent Dirichlet Allocation ” by Blei et al. 2003) “ Support-vector networks ” by Cortes and Vapnik (1995) Significant people : David Blei Corinna Cortes Vladimir Vapnik 4. Companies like Google , Yahoo and Meta pioneered research in this field.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Lexalytics

APRIL 5, 2021

.” And Simard, Steinkraus and Platt [ 27 ] say: “After being extremely popular in the early 1990s, neural networks have fallen out of favor in research in the last 5 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 (!) Hinton (again!)

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Unite.AI

APRIL 2, 2024

These deep learning image restoration models propose to use neural networks based on Transformers and Convolutional Neural Networks. Recent deep learning methods have displayed stronger and more consistent performance when compared to traditional image restoration methods.

Computer Vision 130

Computer Vision Neural Network Convolutional Neural Networks Deep Learning 130

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.

BERT 83

BERT Natural Language Processing Large Language Models NLP 83

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.

Generative AI 119

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

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.

Neural Network 56

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 56

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.

AI Development 52

AI Development AI Developer Computer Vision BERT 52

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.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

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 ).

Machine Learning 52

Machine Learning BERT Neural Network Convolutional Neural Networks 52

DataRobot Blog

MARCH 9, 2022

Now we’re dealing with the same words except they’re surrounded by additional information that changes the tone of the overall message from positive to sarcastic. The Sentiment140 Dataset provides valuable data for training sentiment models to work with social media posts and other informal text. You’re so smart! It provides 1.6

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

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. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. It synthesizes the information from both the image and prompt encoders to produce accurate segmentation masks. Convolutional Neural Networks (CNNs) CNNs are integral to the image encoder of the Segment Anything Model architecture.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 52

Pickl AI

DECEMBER 9, 2024

Inductive bias is crucial in ensuring that Machine Learning models can learn efficiently and make reliable predictions even with limited information by guiding how they make assumptions about the data. This bias allows algorithms to make informed guesses when faced with incomplete or sparse data.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Heartbeat

OCTOBER 12, 2023

This approach aims to equip AI systems with the ability to understand and make sense of the world analogous to human perception, where information is not limited to words but extends to the rich tapestry of sensory experiences like visual, audio, etc. These adaptations allow LLMs to handle a broader spectrum of data types.

Large Language Models 59

Large Language Models Neural Network Convolutional Neural Networks Artificial Intelligence 59

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 to perform or improve AI and NLP business tasks, as well as streamline enterprise workflows.

Neural Network 52

Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

Viso.ai

MARCH 26, 2024

Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ). Attention Mechanisms in Deep Learning Attention mechanisms are helping reimagine both convolutional neural networks ( CNNs ) and sequence models.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Heartbeat

FEBRUARY 8, 2024

Photo in pexel.com With technological advancements, many multimedia data requests efficient ways to search for and obtain information across several methodologies. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Viso.ai

DECEMBER 18, 2023

Known for its efficiency in training convolutional neural networks, CNTK is especially notable in speech and image recognition tasks. The ONNX documentation provides comprehensive and accessible resources for users, offering detailed information, guides, and examples – source. Microsoft Cognitive Toolkit (CNTK).

Explainability 52

Explainability Neural Network Deep Learning Machine Learning 52

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 2: Automated Document Analysis and Processing Computer vision can automate the extraction, analysis, and validation of document information. 1: Fraud Detection and Prevention No.2:

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

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.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Marktechpost

JUNE 27, 2023

The input is also nonlinearly transformed to improve performance on a sophisticated neural network. Any information in the 1 to -1 can have its output normalized with the activation function. Since this is a crucial component of any deep learning or convolutional neural network system.

Neural Network 52

Neural Network Convolutional Neural Networks Deep Learning NLP 52

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] A model for learning to encode patient information into a useful vector representation.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

ML Review

FEBRUARY 11, 2019

If you don’t know it already, NLP had a huge hype of transfer learning in this past 1 year, starting with ULMFit , ELMo , GLoMo , OpenAI transformer , BERT and recently Transformer-XL for further improving language modeling capabilities of the current state of the art. Let’s take a look at an example.

Neural Network 52

Neural Network NLP Explainability Convolutional Neural Networks 52

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). Source: Garg et al ?

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

Sebastian Ruder

JANUARY 19, 2021

However, storing such knowledge implicitly in the parameters of a model is inefficient and requires ever larger models to retain more information. 2020 ) and leak information ( Song & Raghunathan, 2020 ), to be susceptible to backdoors after fine-tuning, which let an attacker manipulate the model prediction ( Kurita et al.,

NLP 52

NLP ML Computer Vision Natural Language Processing 52

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.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

AWS Machine Learning Blog

FEBRUARY 24, 2023

We summarize the insights and conclusion from our test results to help you make an informed decision on configuring your own deployments. The impact is more for models using a convolutional neural network (CNN). We tested two NLP models: bert-base-uncased (109M) and roberta-large (335M).

BERT 99

BERT NLP Computer Vision Neural Network 99

Marktechpost

APRIL 20, 2024

Deep neural networks like convolutional neural networks (CNNs) have revolutionized various computer vision tasks, from image classification to object detection and segmentation. At the heart of ReffAKD lies a carefully crafted convolutional autoencoder (CAE).

Machine Learning 102

Machine Learning Neural Network Convolutional Neural Networks Computer Vision 102