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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Dataconomy

DECEMBER 4, 2023

They are designed to mimic the brain’s ability to process information efficiently and with low power consumption, making them a promising solution for the demanding computational needs of AI tasks. Reduced latency NPUs offer lower latency compared to CPUs and GPUs, meaning they can process data and produce results more quickly.

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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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Dataconomy

OCTOBER 22, 2024

In 2019, 65% of graduating North American PhDs in AI opted for industry roles , a significant jump from 44.4% These datasets provide the necessary scale for training advanced machine learning models, which would be difficult for most academic labs to collect independently.

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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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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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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

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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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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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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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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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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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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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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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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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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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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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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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

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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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. It is not surprising that it has become a major application area for deep learning.

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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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Smart Data Collective

FEBRUARY 24, 2021

Machine Learning to Write your College Essays. Earlier in 2019, the AI development company OpenAI developed a text-writing algorithm named GPT-2 that could use machine learning to generate content. In order to achieve this, Grammarly’s technology combines machine learning with natural language processing approaches.

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Smart Data Collective

MARCH 26, 2021

Due to its constant learning and evolution, the algorithms are able to adapt based on success and failure. Machine learning mimics the human brain. It entails deep learning from its neural networks, natural language processing (NLP), and constant changes based on incoming information.

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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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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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Heartbeat

JUNE 28, 2023

In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT? ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. In this article, you can learn how distillation can be applied to ALBERT to reduce its size and improve its efficiency.

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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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DrivenData Labs

DECEMBER 10, 2023

degree in AI and ML specialization from Gujarat University, earned in 2019. He also boasts several years of experience with Natural Language Processing (NLP). Itay possesses experience in machine learning, deep learning, and full stack development. He holds an M.S.

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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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Dataconomy

MARCH 15, 2023

Visual ChatGPT brings AI image generation to the popular chatbot Back to the present: GPT-4 is released The much-anticipated release of GPT-4 is now available to some Plus subscribers, featuring a new multimodal language model that accepts text, speech, images, and video as inputs and provides text-based answers.

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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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Towards AI

JULY 19, 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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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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AWS Machine Learning Blog

OCTOBER 11, 2024

data # Assing local directory path to a python variable local_data_path = ". . This bucket will be used as source for vector databases and uploading source files. This bucket will be used as source for vector databases and uploading source files.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Advances in neural information processing systems 32 (2019). Journal of machine learning research 9, no. Haibo Ding is a senior applied scientist at Amazon Machine Learning Solutions Lab. He is broadly interested in Deep Learning and Natural Language Processing. Selvaraju, Ramprasaath R.,

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DrivenData Labs

DECEMBER 7, 2023

His main research interests revolve around applications of Network Analysis and Natural Language Processing methods. Amongst other ML competitions, I have been in a prize-winning position for NASA SOHO comet search, NOAA Precipitation Prediction (Rodeo 2), the Spacenet-8 flood detection, and 2019 IEEE GRSS data fusion contest.

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