Pickl AI

JULY 12, 2023

1. Matrix (1999-2011) “The Matrix” trilogy, directed by the Wachowski siblings, is a groundbreaking science fiction series that explores themes of reality, consciousness, and the power of data and information. 10 Best Data Science Movies you need to Watch!

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Data Science Robotics Artificial Intelligence Artificial Intelligence 52

Ehud Reiter

APRIL 9, 2023

Back in 2010, Saad Mahamood (then a PhD student with me) developed an NLG system which summarised data about sick babies in neonatal intensive care for their parents ( Mahamood and Reiter 2011 ); this system was deployed and used in the hospital for a few years, and parents in general were very appreciative.

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ChatGPT Explainability LLM Chatbots 100

Mlearning.ai

JULY 22, 2023

This approach allows Apple to effectively remove a number of privacy concerns by eliminating the need for extensive data sharing and having the algorithms operate locally on users’ devices. Siri launched back in 2011 and became the first modern virtual assistant of its kind.

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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 74

Heartbeat

OCTOBER 9, 2023

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. These ground-breaking areas redefine how we connect with and learn from our collective past.

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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. Most feature-importance algorithms deal very well with dense and categorical features. We will cover very rudimentary methods, along with quite sophisticated algorithms. The dataset has 10 dense features.

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Python Categorization Machine Learning Explainability 52

Mlearning.ai

AUGUST 6, 2023

As described in the previous article , we want to forecast the energy consumption from August of 2013 to March of 2014 by training on data from November of 2011 to July of 2013. Experiments Before moving on to the experiments, let’s quickly remember what’s our task.

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Python Software Development Algorithm AI 52

Viso.ai

FEBRUARY 28, 2023

The OpenCV library contains over 2500 algorithms, extensive documentation, and sample code for real-time computer vision. Since 2011, OpenCV provides functionality for NVIDIA CUDA and Graphic Processing Unit (GPU) hardware acceleration and Open Computing Language (OpenCL). and fleeting them into one or multiple vision algorithms.

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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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Machine Learning ML Categorization Data Scientist 78

Flipboard

MARCH 18, 2023

Some of his early published work on the question, from 2011 and 2012, raises questions about what shape those models will take, and how hard it would be to make developing them go well — all of which will only look more important with a decade of hindsight.

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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

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. So it’s only around 2013, 2011, where AI became a thing in the industry. And neural networks now has become deep learning.

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AI AI Neural Network Natural Language Processing 52

Explosion

FEBRUARY 18, 2015

The short story is, there are no new killer algorithms. The way that the tokenizer works is novel and a bit neat, and the parser has a new feature set, but otherwise the key algorithms are well known in the recent literature. Dependency Parser The parser uses the algorithm described in my 2014 blog post. 0.2%) difference.

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Algorithm Python 40

Topbots

JUNE 22, 2023

Outperforming algorithmic trading reinforcement learning systems: A supervised approach to the cryptocurrency market. Journal of Finance (2011), 66(1):35–65. Proceedings of the Second ACM International Conference on AI in Finance (ICAIF ‘21). Felizardo, L. Lima Paiva, F. de Vita Graves, C.; Matsumoto, E. Del-Moral-Hernandez, E.;

Machine Learning 111

Machine Learning ChatGPT Natural Language Processing Categorization 111

NVIDIA

DECEMBER 13, 2023

The San Francisco-based company has completed more than 800,000 deliveries in seven countries since its start in 2011. You can pick the right place for your algorithms to run to make sure you’re getting the most out of the hardware and the power that you are putting into the system,” said Frantz.

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Unite.AI

JUNE 22, 2023

Two years later, in 2011, I co-founded Crashlytics, a mobile crash reporting tool which was acquired by Twitter in 2013 and then again by Google in 2017. Can you discuss the types of machine learning algorithms that are used? We were acquired by Box in 2009.

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Large Language Models Machine Learning Deep Learning ChatGPT 325

Hal Daumé III

NOVEMBER 8, 2016

These three are: data collection objective function feedback loops Sample Selection Bias The standard way that machine learning works is to take some samples from a population you care about, run it through a machine learning algorithm, to produce a predictor. Also a highly recommended read.

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ML Robotics Machine Learning AI 52

AWS Machine Learning Blog

OCTOBER 10, 2023

Challenges in FL You can address the following challenges using algorithms running at FL servers and clients in a common FL architecture: Data heterogeneity – FL clients’ local data can vary (i.e., Despite these challenges of FL algorithms, it is critical to build a secure architecture that provides end-to-end FL operations.

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ML Algorithm Machine Learning Neural Network 92

IBM Journey to AI blog

OCTOBER 12, 2023

These models rely on learning algorithms that are developed and maintained by data scientists. For example, Apple made Siri a feature of its iOS in 2011. In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training.

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AWS Machine Learning Blog

OCTOBER 23, 2023

Founded in 2011, Talent.com is one of the world’s largest sources of employment. The performance of Talent.com’s matching algorithm is paramount to the success of the business and a key contributor to their users’ experience.

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NLP Deep Learning Machine Learning Natural Language Processing 88

Sebastian Ruder

JULY 31, 2020

At a higher level, algorithms are biased and discriminate against speakers of non-English languages or simply with different accents. A classic example are n-gram language models, which perform significantly worse for languages with elaborate morphology and relatively free word order ( Bender, 2011 ).

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NLP Natural Language Processing Machine Learning ML 78

Lexalytics

APRIL 5, 2021

This would change in 1986 with the publication of “Parallel Distributed Processing” [ 6 ], which included a description of the backpropagation algorithm [ 7 ]. In retrospect, this algorithm seems obvious, and perhaps it was. We were definitely in a Kuhnian pre-paradigmatic period. It would not be the last time that happened.)

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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Sebastian Ruder

NOVEMBER 14, 2022

Bender [2] highlighted the need for language independence in 2011. We can modify the algorithm to prefer tokens that are shared across many languages [146] , preserve tokens’ morphological structure [147] , or make the tokenization algorithm more robust to deal with erroneous segmentations [148]. 21–30).

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Natural Language Processing NLP Computational Linguistics AI 52

Sebastian Ruder

OCTOBER 26, 2019

Cross-lingual learning in the transfer learning taxonomy ( Ruder, 2019 ) Methods from domain adaptation have also been applied to cross-lingual transfer ( Prettenhofer & Stein, 2011 , Wan et al., For a clearer distinction between domain adaptation and cross-lingual learning, have a look at this section. 2018 ; Hartmann et al.,

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BERT NLP Natural Language Processing Neural Network 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.

Computer Vision 52

Computer Vision Software Development AI AI 52

Explosion

DECEMBER 17, 2013

Today, almost all high-performance parsers are using a variant of the algorithm described below (including spaCy). This doesn’t just give us a likely advantage in learnability; it can have deep algorithmic implications. But the parsing algorithm I’ll be explaining deals with projective trees.

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Python Algorithm NLP Computational Linguistics 45

Artificial Corner

JUNE 6, 2023

Data mining involves using sophisticated algorithms to identify patterns and relationships in data that might not be immediately apparent. Its ability to efficiently handle iterative algorithms and machine learning tasks made it a popular choice for data scientists and engineers. Follow Now ? Connect with me on LinkedIn for updates.

Data Mining 52

Data Mining Big Data ETL Machine Learning 52

Mlearning.ai

JUNE 5, 2023

C++ also provides direct access to low-level features like pointers and bitwise operations, which can improve the efficiency of algorithms and data structures. It is a fork of the Python Imaging Library (PIL), which was discontinued in 2011. This is critical for computer vision jobs that need real-time processing and precision.

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Computer Vision Deep Learning Python Neural Network 52

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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ML Data Scientist Big Data Machine Learning 77

Viso.ai

JANUARY 16, 2024

Also, you can use N-shot learning models to label data samples with unknown classes and feed the new dataset to supervised learning algorithms for better training. The following algorithms combine the two approaches to solve the FSL problem. The diagram below illustrates the algorithm. Let’s discuss each in more detail.

Neural Network 52

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Explosion

MARCH 17, 2019

In 2011, deep learning methods were proving successful for NLP, and techniques for pretraining word representations were already in use. This is exactly what algorithms like word2vec, GloVe and FastText set out to solve. This also allows us to avoid depending on the regex module, and instead switch back to Python’s built-in re library.

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NLP Python Neural Network Natural Language Processing 52

ML Review

MAY 3, 2018

Source : Hassanat (2011) [13] These approaches obtained impressive results (over 70% word accuracy) for tests performed with classifiers trained on the same speaker they were tested on. 17] “ LipNet ” introduces the first approach for an end-to-end lip reading algorithm at sentence level. Thus the algorithm is alignment-free.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

AWS Machine Learning Blog

JULY 20, 2023

Ensemble learning refers to the use of multiple learning models and algorithms to gain more accurate predictions than any single, individual learning algorithm. We then provide an example of how you can train, optimize, and deploy your custom ensembles using Amazon SageMaker. References [1] Raj Kumar, P. Arun; Selvakumar, S.

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ML Python Natural Language Processing Machine Learning 81