Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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Data Science Dojo

DECEMBER 8, 2023

Traditional learning approaches Traditional machine learning predominantly relied on supervised learning, a process where models were trained using labeled datasets. In this approach, the algorithm learns patterns and relationships between input features and corresponding output labels.

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Data Science Dojo

OCTOBER 18, 2023

Transformers have revolutionized natural language processing with their use of self-attention mechanisms. Even today, transformers remain the basis of state-of-the-art models such as BERT, Roberta, XLNET, and GPT.

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Dataconomy

SEPTEMBER 4, 2023

Predictive analytics: Predictive analytics leverages historical data and statistical algorithms to make predictions about future events or trends. It’s particularly valuable for forecasting demand, identifying potential risks, and optimizing processes.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

This retrieval can happen using different algorithms. Her research interests lie in Natural Language Processing, AI4Code and generative AI. She received her PhD from Virginia Tech in 2017. He received his PhD from University of Illinois at Urbana-Champaign in 2017.

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AWS Natural Language Processing K-nearest Neighbors Computer Science 119

Towards AI

APRIL 8, 2024

While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance. Word2Vec, a widely-adopted NLP algorithm has proven to be an efficient and valuable tool that is now applied across multiple domains, including recommendation systems.

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Dataconomy

AUGUST 10, 2023

With the application of natural language processing (NLP) and machine learning algorithms, AI systems can understand and translate spoken language into written notes. It can also help with retrieving information from electronic health records (EHRs) and other tasks to alleviate administrative burdens.

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Flipboard

FEBRUARY 5, 2023

It’s a nudge from Duolingo , the popular language-learning app, whose algorithms know you’re most likely to do your 5 minutes of Spanish practice at this time of day. Around 2017, the company started to make a more focused investment in machine learning, and that’s when coauthors Brust and Bicknell joined the team.

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Smart Data Collective

FEBRUARY 24, 2021

You might have received a lengthy email from your coworker, and you could simply press on the ‘Got it’ response suggested by Google’s AI algorithm to compose your reply. Earlier in 2019, the AI development company OpenAI developed a text-writing algorithm named GPT-2 that could use machine learning to generate content.

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AssemblyAI

MARCH 21, 2023

Top 50 keywords in submitted research papers at ICLR 2022 ( source ) A recent bibliometric study systematically analysed this research trend, revealing an exponential growth of published research involving GNNs, with a striking +447% average annual increase in the period 2017-2019.

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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3.

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Applied Data Science

MARCH 11, 2022

Then, we will look at three recent research projects that gamified existing algorithms by converting them from single-agent to multi-agent: ?️‍♀️ All the rage was about algorithms for classification. Rahimi and Recht In last year’s ICRL, researchers presented an algorithm that offered a new perspective on PCA: EigenGame.

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Hacker News

MAY 3, 2023

With these fairly complex algorithms often being described as “giant black boxes” in news and media, a demand for clear and accessible resources is surging. This concept is not exclusive to natural language processing, and has also been employed in other domains. The outcome is the so-called policy model.

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AWS Machine Learning Blog

AUGUST 16, 2023

Our solution is based on the DINO algorithm and uses the SageMaker distributed data parallel library (SMDDP) to split the data over multiple GPU instances. The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. tif" --include "_B03.tif" tif" --include "_B04.tif"

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. It is based on GPT and uses machine learning algorithms to generate code suggestions as developers write.

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Mlearning.ai

APRIL 10, 2023

3 feature visual representation of a K-means Algorithm. Essentially, the clustering algorithm is grouping data points together without any prior knowledge or guidance to discover hidden patterns or unusual data groupings without the need for human interference. 4, center_box=(20, 5)) model = OPTICS().fit(x)

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DataSeries

AUGUST 15, 2024

Deep learning algorithms span a diverse array of architectures, each capable of crafting solutions for a wide range of problem domains. They use their internal state (memory) to process variable-length sequences of inputs, making them ideal for tasks like speech recognition. Explore the most popular types of deep learning architecture.

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Alation

FEBRUARY 20, 2020

Aaron’s background in Symbolic Systems and Linguistics was critical to our realization that data was just another language—comprehensible to machines, but hard to understand for most humans.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing SQL 52

ODSC - Open Data Science

JUNE 13, 2023

billion in 2017 to 3.78 The average annual growth in social media consumers has been 230 million between 2017 and 2021. Social Media Analysis using Natural Language Processing Techniques. Jyotika has been working on Natural Language Processing and Social Media data for 8 years. billion in 2021.

Data Science 52

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Dataconomy

MARCH 7, 2023

AI chatbots have been waiting for you for a while When we talk about AI chat, we mean a conversation between a human and a chatbot that uses natural language processing to simulate human conversation. Its replies are a hybrid of pre-programmed scripts and machine-learning algorithms.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Thirdly, the presence of GPUs enabled the labeled data to be processed. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer. Parallel computing uses these multiple processing elements simultaneously to solve a problem.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

Deep Learning 52

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Explosion

AUGUST 22, 2017

A lot of work has gone into designing optimisation algorithms that are less sensitive to initialisation. ML — Jason Eisner (@adveisner) August 12, 2017 E.g., regularize toward word embeddings θ that were pretrained on big data for some other objective. Coeff controls bias/variance tradeoff.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Source: Lu et al. 2016)[ 91 ] You et al.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

We design an algorithm that automatically identifies the ambiguity between these two classes as the overlapping region of the clusters. This is achieved through the Guided GradCAM algorithm ( Ramprasaath et al. ). Advances in neural information processing systems 30 (2017). probability and Cover 1 Man with 31.3%

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PyImageSearch

JANUARY 2, 2023

Machine learning algorithms can also recognize patterns in DNA sequences and predict a patient’s probability of developing an illness. These algorithms can design potential drug therapies, identify genetic causes of disease, and help understand the mechanisms underlying gene expression.

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AWS Machine Learning Blog

SEPTEMBER 6, 2023

You can easily try out these models and use them with SageMaker JumpStart, which is a machine learning (ML) hub that provides access to algorithms, models, and ML solutions so you can quickly get started with ML. preprocessing_num_workers – The number of processes to use for preprocessing. Must be an integer greater than 1.

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Mlearning.ai

JUNE 6, 2023

HOGs are great feature detectors and can also be used for object detection with SVM but due to many other State of the Art object detection algorithms like YOLO, SSD, present out there, we don’t use HOGs much for object detection. We have the IPL data from 2008 to 2017. This is a simple project.

Machine Learning 52

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Explosion

AUGUST 1, 2019

Transformers and transfer-learning Natural Language Processing (NLP) systems face a problem known as the “knowledge acquisition bottleneck”. The priority of wordpiece tokenizers is to limit the vocabulary size, as vocabulary size is one of the key challenges facing current neural language models ( Yang et al.,

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Dataconomy

APRIL 25, 2023

These tools use machine learning, natural language processing, computer vision, and other AI techniques to provide you with powerful features and functionalities. Marketo can help you segment your audience, personalize your content, nurture your leads, and optimize your conversions using AI-driven algorithms and models.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading! Vive Differentiable Programming!

Deep Learning 52

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Dataconomy

SEPTEMBER 16, 2024

Considering that some languages, notably English, seem to dominate digitally, there is actually a tremendous need for tools that can work across different languages and carry out diverse tasks. Over the past few years, numerous tools have emerged based on multilingual models for natural language processing (NLP).

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DrivenData Labs

DECEMBER 11, 2024

The existence of better dataand in cases like ChatGPT, simply more datahas led to new ways to find patterns across populations, powering algorithms from cancer detection to your Spotify recommendations. This was a clear case where relying on an algorithm without appropriate human review had an unacceptably high human cost.

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