Pickl AI

JULY 24, 2024

Turing proposed the concept of a “universal machine,” capable of simulating any algorithmic process. The development of LISP by John McCarthy became the programming language of choice for AI research, enabling the creation of more sophisticated algorithms. Simon, demonstrated the ability to prove mathematical theorems.

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AWS Machine Learning Blog

OCTOBER 23, 2023

This post is co-authored by Anatoly Khomenko, Machine Learning Engineer, and Abdenour Bezzouh, Chief Technology Officer at Talent.com. Founded in 2011, Talent.com is one of the world’s largest sources of employment. It’s designed to significantly speed up deep learning model training. The model is replicated on every GPU.

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NLP Deep Learning Machine Learning Natural Language Processing 116

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. The AI community categorizes N-shot approaches into few, one, and zero-shot learning. The diagram below illustrates the algorithm. Get a demo here.

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Algorithm 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. As a result of Pascal VOC, researchers, and developers were able to compare various algorithms and methods on an entity basis. Get a demo here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 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. If you want to customize the aggregation algorithm, you need to modify the fedAvg() function and the output.

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ML Algorithm Machine Learning Neural Network 120

Heartbeat

OCTOBER 9, 2023

These ground-breaking areas redefine how we connect with and learn from our collective past. Computer vision algorithms can reconstruct a highly detailed 3D model by photographing objects from different perspectives. But computer vision algorithms can assist us in digitally scanning and preserving these priceless manuscripts.

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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). If you have a large dataset, the SageMaker KNN algorithm may provide you with an effective semantic search.

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LLM Algorithm Machine Learning Natural Language Processing 91

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. This would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ].

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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Viso.ai

FEBRUARY 28, 2023

The OpenCV library contains over 2500 algorithms, extensive documentation, and sample code for real-time computer vision. Because machine learning is essential in computer vision, OpenCV contains a complete, general-purpose ML Library focused on statistical pattern recognition and clustering.

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Mlearning.ai

JULY 30, 2023

What we are looking for in these algorithms is to output a list of features along with corresponding importance values. With most ML use cases moving to deep learning, models’ opacity has increased significantly. Most feature-importance algorithms deal very well with dense and categorical features.

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Python Categorization Machine Learning Explainability 52

AWS Machine Learning Blog

APRIL 6, 2023

We also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm. 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 next calculated the MAPE for the actual sales values.

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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.

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Automation ML Machine Learning Explainability 52

Viso.ai

MARCH 1, 2023

The idea of low-code was introduced in 2011. Computer vision using the YOLOv7 algorithm – built with Viso Suite What is No Code? 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.

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Kavita Ganesan

MAY 24, 2022

And they may not fit in within your infrastructure, you may have an old infrastructure that can maybe take in basic computer algorithms, not something sophisticated that needs GPUs, and TPUs. And neural networks now has become deep learning. So it’s only around 2013, 2011, where AI became a thing in the industry.

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AWS Machine Learning Blog

APRIL 19, 2023

We use Amazon SageMaker to train a model using the built-in XGBoost algorithm on aggregated features created from historical transactions. It’s easy to learn Flink if you have ever worked with a database or SQL-like system by remaining ANSI-SQL 2011 compliant.

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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.,

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Natural Language Processing NLP Computational Linguistics BERT 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.

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NLP Python Neural Network Natural Language Processing 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.

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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.

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