Dataconomy

JUNE 15, 2023

This breakthrough enabled faster and more powerful computations, propelling AI research forward One notable public achievement during this time was IBM’s AI system, Watson, defeating two champions on the game show Jeopardy in 2011. This demonstrated the astounding potential of machines to learn and differentiate between various objects.

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IBM Journey to AI blog

OCTOBER 12, 2023

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. 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. IBM watsonx.ai Explore watsonx.ai

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AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

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Hacker News

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! The concepts behind this kind of text mining have remained fairly constant over the years.

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AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. 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).

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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.

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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.

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Pickl AI

JULY 24, 2024

Key milestones include the Turing Test, the Dartmouth Conference, and breakthroughs in machine learning. Researchers began to focus on Machine Learning, a subfield of AI that emphasises the importance of data-driven approaches. This shift allowed systems to learn from experience and improve their performance over time.

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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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AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. 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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Mlearning.ai

JULY 30, 2023

Identifying important features using Python Introduction Features are the foundation on which every machine-learning model is built. Different machine-learning paradigms use different terminologies for features such as annotations, attributes, auxiliary information, etc. What is feature importance? XGBoost, LightGBM).

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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.

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ML Review

MAY 3, 2018

The addition of language may eventually provide a means for machines to group, reason and articulate complex concepts in the future. 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.

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Explosion

APRIL 27, 2023

We will build a machine learning model in Thinc, implement a new spaCy component, train it with the new configuration system and demonstrate how to use a pre-trained transformer model from the Hugging Face Transformers library to boost your performance. This requires three main steps.

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Hacker News

FEBRUARY 14, 2023

And—as we’ll discuss later—these weights are normally determined by “training” the neural net using machine learning from examples of the outputs we want.) In each case, as we’ll explain later, we’re using machine learning to find the best choice of weights.

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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. Ekanayake, B.,

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DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. When the FRB’s guidance was first introduced in 2011, modelers often employed traditional regression -based models for their business needs.

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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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Data Science Blog

MARCH 14, 2023

Big Data tauchte als Buzzword meiner Recherche nach erstmals um das Jahr 2011 relevant in den Medien auf. Von Data Science spricht auf Konferenzen heute kaum noch jemand und wurde hype-technisch komplett durch Machine Learning bzw. Big Data wurde zum Business-Sprech der darauffolgenden Jahre. ChatGPT basiert auf GPT-3.5

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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.

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Smart Data Collective

MARCH 16, 2020

Deep learning is likely to play an essential role in keeping costs in check. Deep Learning is Necessary to Create a Sustainable Medicare for All System. He should elaborate more on the benefits of big data and deep learning. A lot of big data experts argue that deep learning is key to controlling costs.

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