Marktechpost

JULY 23, 2023

A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. Autoencoder deep learning models are a more traditional alternative to GANs because they are easier to train and produce more diverse outputs.

Machine Learning 98

Machine Learning Data Scarcity Algorithm AI Research 98

Heartbeat

JULY 27, 2023

jpg': {'class': 111, 'label': 'Ford Ranger SuperCab 2011'}, '00236.jpg': Training with TFRecords vs Raw Input Most deep learning tutorials, both Pytorch and Tensorflow, typically show you how to prepare your data for model training by using simple DataGenerators which read the raw data.

Deep Learning 52

Deep Learning Metadata Python Explainability 52

NYU Center for Data Science

SEPTEMBER 11, 2024

What sets Dr. Ho apart is her pioneering work in applying deep learning techniques to astrophysics. She led the first effort to accelerate astrophysical simulations with deep learning. Ho’s contributions have not gone unnoticed.

Convolutional Neural Networks 57

Convolutional Neural Networks Neural Network Deep Learning Data Science 57

AWS Machine Learning Blog

DECEMBER 14, 2023

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Established in 2011, Talent.com aggregates paid job listings from their clients and public job listings, and has created a unified, easily searchable platform.

ETL 120

ETL Data Scientist Machine Learning Deep Learning 120

Marktechpost

SEPTEMBER 24, 2024

However, this work demonstrated that with sufficient data and computational resources, deep learning models can learn complex features through a general-purpose algorithm like backpropagation. Further, pre-training on the ImageNet Fall 2011 dataset, followed by fine-tuning, reduced the error to 15.3%. Check out the Paper.

Convolutional Neural Networks 64

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 64

Viso.ai

FEBRUARY 11, 2024

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. 2011 – A good ILSVRC image classification error rate is 25%.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 52

NVIDIA

NOVEMBER 15, 2023

But the machine learning engines driving them have grown significantly, increasing their usefulness and popularity. IBM’s Watson became a TV celebrity in 2011 when it handily beat two human champions on the Jeopardy! Today, LLMs are taking question-answering systems to a whole new level.

Generative AI 145

Generative AI LLM Neural Network AI Modeling 145

Pickl AI

JULY 24, 2024

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 52

AWS Machine Learning Blog

APRIL 6, 2023

For example, in the 2019 WAPE value, we trained our model using sales data between 2011–2018 and predicted sales values for the next 12 months (2019 sale). We trained three models using data from 2011–2018 and predicted the sales values until 2021. He focuses on machine learning, deep learning and end-to-end ML solutions.

Machine Learning 113

Machine Learning ML Categorization Data Scientist 113

Viso.ai

JANUARY 16, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. On the Caltech-USCD Birds-200-2011 (CUB-200-2011) dataset, the SOTA ZSL model stands at a 72.3 Get a demo here. The CLIP model for ZSL shows 64.3%

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Algorithm 52

AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets. Her current areas of interest include federated learning, distributed training, and generative AI.

ML 128

ML Algorithm Machine Learning Neural Network 128

John Snow Labs

DECEMBER 26, 2023

The creation of the LSTM-based sentiment analysis model will provide a thorough method for using deep learning techniques for analyzing human sentiment from textual data, leveraging PyTorch’s flexibility and efficiency. Learning Word Vectors for Sentiment Analysis. abs/2005.03993 Andrew L. Maas, Raymond E. Daly, Peter T.

Neural Network 52

Neural Network Computational Linguistics NLP Natural Language Processing 52

Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here. Find the ImageNet paper here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

MAY 31, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. VOC2011 PASCAL VOC challenge took a big step forward in 2011 with VOC2011. provides a robust end-to-end computer vision infrastructure – Viso Suite.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Explosion

FEBRUARY 12, 2017

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. The SNLI dataset is over 100x larger than previous similar resources, allowing current deep-learning models to be applied to the problem.

Neural Network 40

Neural Network Deep Learning Natural Language Processing NLP 40

Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). He focuses on developing scalable machine learning algorithms.

LLM 101

LLM Algorithm Machine Learning Natural Language Processing 101

Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. Preservation of cultural heritage and natural history through game-based learning. Ahmad, M., & Selviandro, N.

Computer Vision 52

Computer Vision Algorithm Deep Learning Machine Learning 52

Viso.ai

FEBRUARY 28, 2023

Because machine learning is essential in computer vision, OpenCV contains a complete, general-purpose ML Library focused on statistical pattern recognition and clustering. Since 2011, OpenCV provides functionality for NVIDIA CUDA and Graphic Processing Unit (GPU) hardware acceleration and Open Computing Language (OpenCL).

Computer Vision 52

Computer Vision Algorithm Deep Learning Automation 52

AWS Machine Learning Blog

APRIL 19, 2023

It’s easy to learn Flink if you have ever worked with a database or SQL-like system by remaining ANSI-SQL 2011 compliant. About the Authors Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build AI/ML solutions.

ML 96

ML Data Scientist Big Data Machine Learning 96

Kavita Ganesan

MAY 24, 2022

And then he picked up again, I think, around 2011, when big data became a thing, then we had lots of faster computation power, and then it just accelerated from that. And neural networks now has become deep learning. So it’s only around 2013, 2011, where AI became a thing in the industry.

AI 52

AI AI Neural Network Natural Language Processing 52

DataRobot Blog

MAY 26, 2022

When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression -based models for their business needs. While SR 11-7 is prescriptive in its guidance, one challenge that validators face today is adapting the guidelines to modern ML methods that have proliferated in the past few years.

Automation 52

Automation Machine Learning ML Explainability 52

Mlearning.ai

JULY 30, 2023

With most ML use cases moving to deep learning, models’ opacity has increased significantly. Reference Scikit-learn: Machine Learning in Python , Pedregosa et al., 2825–2830, 2011. Reducing the number of features directly reduces training and inference costs and time. JMLR 12, pp. Menze, B.H.,

Python 52

Python Categorization Machine Learning Explainability 52

Explosion

MARCH 17, 2019

It’s widely used in production and research systems for extracting information from text, developing smarter user-facing features, and preprocessing text for deep learning. In 2011, deep learning methods were proving successful for NLP, and techniques for pretraining word representations were already in use.

NLP 52

NLP Python Neural Network Natural Language Processing 52

Sebastian Ruder

DECEMBER 19, 2023

 In all cases, academic research was not left in the dust but went on to make contributions that shaped the next era, from KenLM ( Heafield, 2011 ), an efficient LM library that enabled academics to outperform industry MT systems, to the word2vec alternative GloVe ( Pennington et al., 2018 ) and ULMFiT ( Howard & Ruder, 2018 ).

NLP 52

NLP LLM Large Language Models Computer Scientist 52

Sebastian Ruder

DECEMBER 19, 2023

 In all cases, academic research was not left in the dust but went on to make contributions that shaped the next era, from KenLM ( Heafield, 2011 ), an efficient LM library that enabled academics to outperform industry MT systems, to the word2vec alternative GloVe ( Pennington et al., 2018 ) and ULMFiT ( Howard & Ruder, 2018 ).

NLP 52

NLP LLM Large Language Models Computer Scientist 52

Sebastian Ruder

NOVEMBER 14, 2022

This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. Bender [2] highlighted the need for language independence in 2011. The Deep Learning Indaba 2022 in Tunesia. I've tried to cover as many contributions as possible but undoubtedly missed relevant work. ↩︎ Ruder, S.,

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

Viso.ai

MARCH 1, 2023

The idea of low-code was introduced in 2011. has been a leader in AI vision software to create custom computer vision and deep learning applications that process video feeds of numerous cameras in real-time with deployed AI algorithms. Drag-and-drop editor of a low-code/no-code computer vision platform Viso Suite.

Computer Vision 52

Computer Vision Software Development AI AI 52

Explosion

APRIL 27, 2023

The machine learning model So let’s start with the first step of building the machine learning model. We’ll be using our own deep learning library, Thinc , which is lightweight and offers a functional programming API for composing neural networks. This powerful approach provides a lot of flexibility and transparency.

NLP 69

NLP Neural Network Machine Learning Deep Learning 69

Unite.AI

OCTOBER 10, 2024

JPEG-DL Instead, the new work , titled JPEG Inspired Deep Learning , offers a much simpler architecture, which can even be imposed upon existing models. However, freezing parts of a model during training tends to reduce the versatility of the model, as well as its broader resilience to novel data.

Neural Network 162

Neural Network Convolutional Neural Networks Deep Learning Explainability 162

ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

Mlearning.ai

JUNE 5, 2023

Many Libraries: Python has many libraries and frameworks (We will be looking some of them below) that provide ready-made solutions for common computer vision tasks, such as image processing, face detection, object recognition, and deep learning. It is a fork of the Python Imaging Library (PIL), which was discontinued in 2011.

Computer Vision 52

Computer Vision Deep Learning Python Neural Network 52

AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. The distribution is versioned using SemVer and will be released on a regular basis moving forward.

ML 110

ML Generative AI Data Scientist Data Science 110