Dataconomy

FEBRUARY 19, 2025

BERT is an open source machine learning framework for natural language processing (NLP) that helps computers understand ambiguous language by using context from surrounding text. In October 2019, Google applied BERT to its U.S.-based

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Data Science Dojo

OCTOBER 10, 2023

Building bridges : Think of a young developer who attended an AI conference back in 2019. Learning from real-world applications : Who doesn’t want to revolutionize their manufacturing process by integrating AI, a strategy learned from a case study at an AI conference.

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Generating Wikipedia By Summarizing Long Sequences This work was published by Peter J Liu at Google in 2019.

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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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IBM Journey to AI blog

AUGUST 13, 2024

An early hint of today’s natural language processing (NLP), Shoebox could calculate a series of numbers and mathematical commands spoken to it, creating a framework used by the smart speakers and automated customer service agents popular today.

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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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Towards AI

FEBRUARY 10, 2023

AI and Climate change Mining for AI: Natural Resource Extraction To understand what AI is made from, we need to leave Silicon Valley and go to the place where the stuff for the AI industry is made. The cloud, which consists of vast machines, is arguably the backbone of the AI industry. By comparison, Moore’s Law had a 2-year doubling period.

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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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AWS Machine Learning Blog

OCTOBER 5, 2023

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. an AI start-up, and worked as the CEO and Chief Scientist in 2019–2021. He founded StylingAI Inc., Before joining the industry, he was the Charles E.

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Dataversity

FEBRUARY 16, 2022

Next in our blog series exploring interesting analytics use cases, we examine how machine learning algorithms dictate the music we listen to every day. In 2019, the music streaming market was valued at $12,831.2 million – a figure that’s expected to nearly double by 2027.

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Smart Data Collective

JULY 23, 2021

billion in 2019. To perform its function , a chatbot will use advanced machine learning and natural language processing algorithms. The latter is also known as NLP, which refers to the ability of the computer to process, understand, and respond in a human language. billion by 2024 compared to $2.6

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Hacker News

MARCH 25, 2022

In addition, the math that transformers use lends itself to parallel processing, so these models can run fast. Transformers now dominate popular performance leaderboards like SuperGLUE , a benchmark developed in 2019 for language-processing systems. How Transformers Pay Attention.

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Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately? Algorithms can automatically detect and extract key items.

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Mlearning.ai

JUNE 14, 2023

Learning LLMs (Foundational Models) Base Knowledge / Concepts: What is AI, ML and NLP Introduction to ML and AI — MFML Part 1 — YouTube What is NLP (Natural Language Processing)? — YouTube YouTube Introduction to Natural Language Processing (NLP) NLP 2012 Dan Jurafsky and Chris Manning (1.1)

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Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. Just wait until you hear what happened in 2022. Who should I follow?

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Dataconomy

OCTOBER 27, 2023

2 uses natural language processing to generate imagery based on your text prompts. Assuming you approve its suggested changes, the tool automatically makes them for you, dealing with any coding that needs to take place in the process. million in its latest funding round.

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DrivenData Labs

FEBRUARY 3, 2025

His research focuses on applying natural language processing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings. I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs.

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Smart Data Collective

DECEMBER 19, 2019

© Statista 2019 Also, here is a list of some other stats. AI algorithm learns from data pool – we already know that. Lack of understanding the algorithm limitations. Excessive dependence on a single AI algorithm. Augmenting learning along with analysis through natural language processing.

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How to Learn Machine Learning

JULY 20, 2024

It falls under machine learning and uses deep learning algorithms and programs to create music, art, and other creative content based on the user’s input. This trend involves integrating advanced AI algorithms into various software and platforms, improving user experiences with personalized, intelligent functionalities.

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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

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.

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Heartbeat

OCTOBER 10, 2023

Machine learning algorithms can process and analyze enormous volumes of data, which enables them to grow and learn over time. However, ensuring the algorithms are transparent and ethical is one of the most significant difficulties. It was developed by Facebook AI Research and released in 2019.

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Heartbeat

JANUARY 29, 2024

At its core, Meta-Learning equips algorithms with the aptitude to quickly grasp new tasks and domains based on past experiences, paving the way for unparalleled problem-solving skills and generalization abilities. MAML trains a model's initial parameters to fine-tune rapidly for new tasks with just a few examples.

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Pickl AI

JANUARY 3, 2024

Career Advancement: Professionals can enhance earning potential by acquiring in-demand skills like Natural Language Processing, Deep Learning, and relevant certifications aligned with industry needs. Geographic Variations: The average salary of a Machine Learning professional in India is ₹12,95,145 per annum.

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Explosion

NOVEMBER 21, 2019

Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. al, 2015) is a twist on the word2vec family of algorithms that lets you learn more interesting word vectors. Contrasting the results between the 2015 and 2019 models provides an interesting new avenue to explore.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

“Data locked away in text, audio, social media, and other unstructured sources can be a competitive advantage for firms that figure out how to use it“ Only 18% of organizations in a 2019 survey by Deloitte reported being able to take advantage of unstructured data. The majority of data, between 80% and 90%, is unstructured data.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

While this data holds valuable insights, its unstructured nature makes it difficult for AI algorithms to interpret and learn from it. According to a 2019 survey by Deloitte , only 18% of businesses reported being able to take advantage of unstructured data. Clean data is important for good model performance.

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Flipboard

JULY 10, 2023

Figure 7: The topology of Sparse Autoencoder (source: Shi, Ji, Zhang, and Miao, “Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification,” International Journal of Advanced Robotic Systems , 2019 ). time series or natural language processing tasks).

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AWS Machine Learning Blog

APRIL 19, 2024

This flywheel consists of innovation in developing new and better algorithms, enabling more use-cases and applications, driving wider adoption and demand, which in turn leads to further investment in research and optimization to drive more innovation. What is the numerical difference between the parameter size of GPT-2 and GPT-3?

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Kaggle

JULY 29, 2020

In August 2019, Data Works was acquired and Dave worked to ensure a successful transition. I also have experience in building large-scale distributed text search and Natural Language Processing (NLP) systems. This was an engaging way for me to stay focused not only in the algorithms but the data itself.

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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

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 32 (2019). probability and Cover 1 Man with 31.3%

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Parallel computing uses these multiple processing elements simultaneously to solve a problem. This is accomplished by breaking the problem into independent parts so that each processing element can complete its part of the workload algorithm simultaneously. It also means not all workloads are equally suitable for acceleration.

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Heartbeat

MARCH 15, 2023

Photo by Fatos Bytyqi on Unsplash Introduction Did you know that in the past, computers struggled to understand human languages? But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language.

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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”. 2019) have shown that a transformer models trained on only 1% of the IMDB sentiment analysis data (just a few dozen examples) can exceed the pre-2016 state-of-the-art.

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The MLOps Blog

APRIL 5, 2023

For example, Modularizing a natural language processing (NLP) model for sentiment analysis can include separating the word embedding layer and the RNN layer into separate modules, which can be packaged and reused in other NLP models to manage code and reduce duplication and computational resources required to run the model.

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Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. This is exactly what algorithms like word2vec, GloVe and FastText set out to solve.

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