DataRobot

FEBRUARY 10, 2021

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard and Sylvain Gugger is a hands-on guide that helps people with little math background understand and use deep learning quickly. The following figure shows the Python code and how it led to data after November 2011.

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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.

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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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IBM Journey to AI blog

OCTOBER 12, 2023

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. For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests.

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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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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.

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Hacker News

MARCH 25, 2022

“Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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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! Today, LLMs are taking question-answering systems to a whole new level.

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Dataconomy

AUGUST 23, 2023

This 2011 released game, which is famous for its modding community, has been mentioned again with a sensational mod in recent months. Even in a 2011 game, AI systems can add something so impressive, the use of AI systems in next-gen games is a fascinating concept.

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IBM Data Science in Practice

JULY 5, 2023

These handlers might be complex pre-trained deep learning models, like MolFormer or ESM, or simple algorithms like the morgan fingerprint. Nucleic Acids Research, 40(D1):D1100–D1107, 09 2011. The handlers take as input the nodes of the KG of a specific modality (e.g.: Overington. ISSN 0305–1048. doi: 10.1093/nar/gkr777.

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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. Ahmad, M., & Selviandro, N.

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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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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.

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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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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.,

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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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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.

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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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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.

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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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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.

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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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Hacker News

FEBRUARY 14, 2023

And in fact the big breakthrough in “deep learning” that occurred around 2011 was associated with the discovery that in some sense it can be easier to do (at least approximate) minimization when there are lots of weights involved than when there are fairly few.

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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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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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