ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. In a change from last year, there’s also a higher demand for those with data analysis skills as well.

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Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019. 2019 ) and other variants. In biology, Transformer language models have been pretrained on protein sequences ( Rives et al., 2019 ), MoCo ( He et al., 2019 ), MoCo ( He et al., 2019 ) and domains ( Desai et al.,

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AWS Machine Learning Blog

OCTOBER 5, 2023

He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. an AI start-up, and worked as the CEO and Chief Scientist in 2019–2021.

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NLP People

JANUARY 2, 2019

Hundreds of researchers, students, recruiters, and business professionals came to Brussels this November to learn about recent advances, and share their own findings, in computational linguistics and Natural Language Processing (NLP). 3-Is Automatic Post-Editing (APE) a Thing?

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Viso.ai

DECEMBER 20, 2023

Huawei’s Mindspore is an open-source deep learning framework for training and inference written in C++. Our no-code solution enables teams to rapidly build real-world computer vision using the latest deep learning models out of the box. Adaptive Learning Rate. Operating under the Apache-2.0 Book a demo.

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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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Flipboard

JULY 10, 2023

Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ). time series or natural language processing tasks). This architecture is well-suited for handling sequential data (e.g.,

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Towards AI

FEBRUARY 10, 2023

Building natural language processing and computer vision models that run on the computational infrastructures of Amazon Web Services or Microsoft’s Azure is energy-intensive. The Myth of Clean Tech: Cloud Data Centers The data center has been a critical component of improvements in computing.

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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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Marktechpost

SEPTEMBER 2, 2024

Recent developments in machine learning, particularly in natural language processing (NLP), have significantly enhanced the capabilities of IR systems. One approach uses large language models (LLMs), which have shown promise in generating relevance judgments that align closely with human assessments.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. The DJL was created at Amazon and open-sourced in 2019. The architecture of DJL is engine agnostic.

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Viso.ai

FEBRUARY 23, 2024

Our software enables ML teams to train deep learning and machine learning models and deploy them in computer vision applications – completely end-to-end. For more details, check out our Image Segmentation Using Deep Learning article. Modern machine learning has come a long way. Get a demo.

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Artificial Corner

AUGUST 3, 2023

The platform can be trained on 3 models which are machine learning, deep learning, and natural language processing (NLP). To improve the prediction, the company used deep learning architecture from 2019 onwards. Why Michelangelo is Required in Uber?

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Bugra Akyildiz

AUGUST 6, 2023

However, it was back in 2018-2019, ChatGPT has not taken over the industry both in terms of model accuracy, but also overall deployment options. Embeddings can be used for a variety of tasks in different downstream applications, such as natural language processing (NLP), recommendation systems, and search engines.

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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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Heartbeat

JANUARY 23, 2023

Image from Hugging Face Hub Introduction Most natural language processing models are built to address a particular problem, such as responding to inquiries regarding a specific area. This restricts the applicability of models for understanding human language. Alex Warstadt et al. print("1-",qqp["train"].homepage)

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How to Learn Machine Learning

JULY 20, 2024

In this article you will learn about 7 of the top Generative AI Trends to watch out for in this year, so please please sit back relax, enjoy, and learn! 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.

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Mlearning.ai

FEBRUARY 19, 2023

You don’t need to have a PhD to understand the billion parameter language model GPT is a general-purpose natural language processing model that revolutionized the landscape of AI. GPT-3 is a autoregressive language model created by OpenAI, released in 2020 . What is GPT-3?

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Towards AI

JULY 21, 2023

The Ninth Wave (1850) Ivan Aivazovsky NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 09.13.20 It leverages an interface across tasks that are grounded on a single knowledge source: the 2019/08/01 Wikipedia snapshot containing 5.9M Aere Perrenius Welcome back. Hope you enjoyed your week!

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Pickl AI

JANUARY 3, 2024

Geographic Variations: The average salary of a Machine Learning professional in India is ₹12,95,145 per annum. 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.

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Heartbeat

JUNE 6, 2023

In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. Once the GCN is trained, it is easier to process new graphs and make predictions about them. Richong, Z., Yongyi, M., & Xudong L.

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AWS Machine Learning Blog

JUNE 13, 2023

The first generation of AWS Inferentia, a purpose-built accelerator launched in 2019, is optimized to accelerate deep learning inference. Conclusion AWS Inferentia2 is a powerful technology designed for improving performance and reducing costs of deep learning model inference.

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., 2019) or by using input pre-processing techniques to remove adversarial perturbations (Xie et al.,

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Heartbeat

OCTOBER 10, 2023

One of the most popular techniques for speech recognition is natural language processing (NLP), which entails training machine learning models on enormous amounts of text data to understand linguistic patterns and structures. It was developed by Facebook AI Research and released in 2019.

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Bugra Akyildiz

JULY 16, 2023

Google created a new learning path guides you through a curated collection of content on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud.

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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?

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Sebastian Ruder

NOVEMBER 14, 2022

Developing models that work for more languages is important in order to offset the existing language divide and to ensure that speakers of non-English languages are not left behind, among many other reasons. This post is partially based on a keynote I gave at the Deep Learning Indaba 2022. Kolesnikov, A.,

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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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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. And indeed we can see other machine learning topics arising to take their place, like “optimization” in the mid-’00s, with “deep learning” springing out of nowhere in 2012.

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Viso.ai

JUNE 12, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. GPT models are based on transformer-based deep learning neural network architecture. About us : Viso.ai Get a demo here. billion parameters.

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Heartbeat

MARCH 22, 2023

RoBERTa: A Modified BERT Model for NLP — by Khushboo Kumari An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019.

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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. Named entity annotations and noun phrases can also help, by letting you learn vectors for multi-word expressions.

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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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Sebastian Ruder

AUGUST 23, 2021

Does this mean that we have solved natural language processing? For instance, the AI Index Report 2021 uses SuperGLUE and SQuAD as a proxy for overall progress in natural language processing. 2019 ); the emergence of multi-task datasets such as GLUE ; and multi-modality datasets such as WILDS.

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Heartbeat

JANUARY 29, 2024

Imagine an AI system that becomes proficient in many tasks through extensive training on each specific problem and a higher-order learning process that distills valuable insights from previous learning endeavors. Reptile is a meta-learning algorithm that falls under model-agnostic meta-learning approaches.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Thirdly, the presence of GPUs enabled the labeled data to be processed. Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. FP64 is used in HPC fields, such as the natural sciences and financial modeling, resulting in minimal rounding errors.

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Viso.ai

JULY 9, 2024

Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. AlphaZero was able to surpass its capabilities relatively quickly from a blank slate using reinforcement learning from games of self-play.

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