IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). He started out with 3,600 olive trees, all of which were killed by Winter Storm Uri in 2021. Well-trained computer vision models produce consistent quantitative data instantly.”

Computer Vision 188

Computer Vision Convolutional Neural Networks Explainability AI 188

Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. The birth of Neural networks was initiated with an approach akin to structuring solving problems with algorithms modeled after the human brain.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Heartbeat

DECEMBER 8, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. years, a 0.16% increase from 2021.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

AWS Machine Learning Blog

DECEMBER 7, 2023

Six algorithms available in Forecast were tested: Convolutional Neural Network – Quantile Regression (CNN-QR), DeepAR+ , Prophet , Non-Parametric Time Series (NPTS), Autoregressive Integrated Moving Average (ARIMA), and Exponential Smoothing (ETS).

Convolutional Neural Networks 112

Convolutional Neural Networks Neural Network Algorithm Machine Learning 112

Artificial Corner

JUNE 23, 2023

Like U-Net, TransUnet includes two main sections: Encoder (Vision Transformer): Vision Transformer encodes patches of feature maps that are generated by a convolution neural network. Houlsby, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020) [3] Jieneng Chen, Yongyi Lu, Qihang Yu, Xiangde Luo, E.

Convolutional Neural Networks 80

Convolutional Neural Networks Neural Network Automation Artificial Intelligence 80

Flipboard

MAY 17, 2023

CEOs of major auto companies were all saying by 2020 or 2021 or 2022, roughly. Convolutional neural networks being able to label regions of an image. So there’s a case where AI, convolutional neural networks—which, by the way, applied to vision is 10 years old—is going to make a difference.

Large Language Models 182

Large Language Models Robotics Convolutional Neural Networks Neural Network 182

Viso.ai

SEPTEMBER 2, 2024

According to the data from the recruiting platforms – job listings that look for artificial intelligence or computer vision specialists doubled from 2021 to 2023. As many areas of artificial intelligence (AI) have experienced exponential growth, computer vision is no exception. It’s definitely an exciting time to be in AI.

Computer Vision 52

Computer Vision Software Engineer Convolutional Neural Networks Neural Network 52

Google Research AI blog

FEBRUARY 23, 2023

In 2021, we shared some of our early work using smartphone cameras to measure heart rate and to help identify skin conditions. Previous work had shown that convolutional neural networks (CNNs) could interpret ultrasounds acquired by trained sonographers using a standardized acquisition protocol.

Convolutional Neural Networks 104

Convolutional Neural Networks Neural Network ML Deep Learning 104

Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). The process of locating and identifying objects of interest in an image or video is part of the object detection method, a popular computer vision technique.

Computer Vision 59

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 59

Viso.ai

APRIL 25, 2024

Today’s boom in CV started with the implementation of deep learning models and convolutional neural networks (CNN). 2021) published their research Anomaly Detection in E-Health Applications Using Lightweight CNN Architecture. Lightweight computer vision models allow the users to deploy them on mobile and edge devices.

Computer Vision 52

Computer Vision Deep Learning Convolutional Neural Networks Python 52

PyImageSearch

SEPTEMBER 23, 2024

in 2021 in their paper titled “ An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale.” The strength of Vision Transformers lies in their ability to model relationships between distant parts of an image, which traditional convolutional neural networks (CNNs) might struggle to capture.

Deep Learning 60

Deep Learning Python Computer Vision Software Development 60

Viso.ai

DECEMBER 10, 2023

The most popular machine learning method is deep learning, where multiple hidden layers of a neural network are used in a model. In comparison, the YOLOR algorithm released in 2021 achieves inference times of 12ms on the same benchmark, surpassing the popular YOLOv4 and YOLOv3 deep learning algorithms.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

AWS Machine Learning Blog

MAY 15, 2023

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.

Convolutional Neural Networks 72

Convolutional Neural Networks Neural Network Metadata Data Scientist 72

Mlearning.ai

NOVEMBER 29, 2023

Legacy Methods of Time Series Forecasting Recurrent Neural Network (RNN) Recurrent Neural Networks process a time series step-by-step, maintaining an internal state from time step to time step. Neural networks are great in this application as they can learn the temporal dependence from the given data.

Machine Learning 52

Machine Learning ML Neural Network Convolutional Neural Networks 52

NVIDIA

OCTOBER 4, 2023

Harnessing the raw power of NVIDIA GPUs and aided by a network of thousands of cameras dotting the Californian landscape, DigitalPath has refined a convolutional neural network to spot signs of fire in real time. And the total dollar damage of wildfires in California from 2019 to 2021 was estimated at over $25 billion.

Convolutional Neural Networks 86

Convolutional Neural Networks Neural Network AI AI 86

Marktechpost

JULY 24, 2023

Up-to-date machine learning-based data-driven techniques directly handle downstream forecasting or projection tasks by training a data-driven functional mapping in deep neural networks. These networks lack numerical model generality since they are trained on limited and consistent climate data for discrete spatiotemporal tasks.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network ML 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. Yes 2021 – today ViT Variants There are several ViT variants, including DeiT, PVT, TNT, Swin, and CSWin (2022).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Viso.ai

JUNE 4, 2024

Many of these methods utilize deep learning and Convolutional Neural Networks (CNNs) to create point cloud processing. Deep Learning Convolutional-Based Method Processing irregular, unstructured point cloud data remains a formidable challenge, despite the deep learning’s success in processing structured 2D image data.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Google Research AI blog

FEBRUARY 3, 2023

Real-time boundary tracking of the 2021-2022 Wrattonbully bushfire , shown as a red polygon in Google Maps. All inputs are resampled to a uniform 1 km–square grid and fed into a convolutional neural network (CNN). Inputs Wildfire boundary tracking requires balancing spatial resolution and update frequency.

Convolutional Neural Networks 108

Convolutional Neural Networks Neural Network Software Engineer Machine Learning 108

DataRobot Blog

MARCH 9, 2022

The company was acquired by DataRobot in 2021. The idea is that instead of performing convolutions on image pixels, the model can instead perform those convolutions in the embedded feature space of the words in a sentence. Because these networks are recurrent, they are ideal for working with sequential data such as text.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Viso.ai

MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Moley Robotic Kitchen In 2021, we witnessed the development of Moley, a robotic chef with two smoothly moving arms and a stack of pre-programmed recipes. Brooks et al.

Robotics 52

Robotics Convolutional Neural Networks Computer Vision Deep Learning 52

Viso.ai

MAY 20, 2023

It requires several times cheaper hardware than other neural networks and can be trained much faster on small datasets without any pre-trained weights. Most algorithms use a convolutional neural network (CNN) to extract features from the image to predict the probability of learned classes.

Algorithm 52

Algorithm Computer Vision Deep Learning Neural Network 52

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

deepsense.ai

MAY 7, 2023

Next, we embed the images using an Inception-based [ 5 ] neural network. This solution is based on several Convolutional Neural Networks that work in a cascade fashion to locate the face with some landmarks in an image. 2022 [link] Going deeper with convolutions , Szegedy et al.

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. His research interests are graph neural networks, computer vision, time series analysis and their industrial applications. and Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ).

Computer Vision 69

Computer Vision Machine Learning ML Convolutional Neural Networks 69

Viso.ai

NOVEMBER 27, 2023

R-CNN (Region-based Convolutional Neural Networks) is one of the most commonly used machine learning models today. Object Detection Applied in Outer Space The high-resolution, high-sensitivity instruments carried by the James Webb Space Telescope, were launched in 2021.

Computer Vision 52

Computer Vision Convolutional Neural Networks Algorithm Deep Learning 52

Sebastian Ruder

JANUARY 19, 2021

 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. 2020 ) employed a CNN to compute image features, later models were completely convolution-free.

NLP 52

NLP ML Computer Vision Neural Network 52

Viso.ai

MAY 14, 2024

Convolutional Neural Network Zhang et al. 2021) created a CNN-based fusion framework to extract features and reconstruct images by using a carefully designed loss function. It splits the original image into regions so that every region is in focus for at least one channel of the image. How to Implement Image Fusion?

Computer Vision 52

Computer Vision Robotics Convolutional Neural Networks Neural Network 52

Google Research AI blog

JANUARY 18, 2023

One trend that started with our work on Vision Transformers in 2020 is to use the Transformer architecture in computer vision models rather than convolutional neural networks. The neural network perceives an image, and generates a sequence of tokens for each object, which correspond to bounding boxes and class labels.

Computer Vision 132

Computer Vision Auto-classification Large Language Models Neural Network 132

Unite.AI

JUNE 25, 2024

These techniques involve training neural networks on large datasets of images and videos, enabling them to generate synthetic media that closely mimics real-life appearances and movements. GANs consist of two neural networks, the generator and the discriminator, working in tandem.

Neural Network 263

Neural Network Convolutional Neural Networks Artificial Intelligence Artificial Intelligence 263