Applied Data Science

DECEMBER 23, 2022

Just wait until you hear what happened in 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. The world was hooked. What happened?

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Dataconomy

FEBRUARY 26, 2024

Their architecture is a beacon of parallel processing capability, enabling the execution of thousands of tasks simultaneously. This attribute is particularly beneficial for algorithms that thrive on parallelization, effectively accelerating tasks that range from complex simulations to deep learning model training.

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Dataversity

JANUARY 31, 2022

The post What to Expect from AI in 2022 appeared first on DATAVERSITY. As AI has become more widespread, accessible, and acceptable, it’s stepped in to fill gaps in the economic, social, institutional, and political realms – […].

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Dataconomy

APRIL 27, 2023

An AI computer, also known as an artificial intelligence computer, is a computer system that is specifically designed to perform tasks that would typically require human intelligence, such as reasoning, problem-solving, and learning. They can also switch between different tasks and learn from new data.

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Pickl AI

NOVEMBER 27, 2024

Summary: Gated Recurrent Units (GRUs) enhance Deep Learning by effectively managing long-term dependencies in sequential data. Their applications span various fields, including natural language processing, time series forecasting, and speech recognition, making them a vital tool in modern AI.

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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. Java has numerous libraries designed for the language, including CoreNLP, OpenNLP, and others.

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Heartbeat

NOVEMBER 21, 2023

Computer vision, the field dedicated to enabling machines to perceive and understand visual data, has witnessed a monumental shift in recent years with the advent of deep learning. Photo by charlesdeluvio on Unsplash Welcome to a journey through the advancements and applications of deep learning in computer vision.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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ODSC - Open Data Science

MARCH 28, 2023

Learn more about this brand new track here ! Machine Learning and Deep Learning This track gathers together the creators and top practitioners in the rapidly expanding fields of deep learning and machine learning to discuss the latest advances, trends, and models in these fields.

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Google Research AI blog

FEBRUARY 28, 2023

In 2022, we expanded our research interactions and programs to faculty and students across Latin America , which included grants to women in computer science in Ecuador. We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia.

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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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AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Founded in 2021, ThirdAI Corp.

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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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Flipboard

JUNE 20, 2023

We present the results of recent performance and power draw experiments conducted by AWS that quantify the energy efficiency benefits you can expect when migrating your deep learning workloads from other inference- and training-optimized accelerated Amazon Elastic Compute Cloud (Amazon EC2) instances to AWS Inferentia and AWS Trainium.

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Pickl AI

DECEMBER 26, 2024

billion by the end of 2024 , reflecting a remarkable increase from $29 billion in 2022. Computer Hardware At the core of any Generative AI system lies the computer hardware, which provides the necessary computational power to process large datasets and execute complex algorithms. What are Foundation Models?

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Flipboard

MARCH 27, 2023

On December 2, 2022, the team announced the launch of PyTorch 2.0, a next-generation release that will make training deep neural networks much faster and support dynamic shapes. In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0. The stable release of PyTorch 2.0 Models (e.g.,

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Heartbeat

FEBRUARY 28, 2023

The past few years have witnessed exponential growth in medical image analysis using deep learning. In this article we will look into medical image segmentation and see how deep learning can be helpful in these cases. This can be further classified as supervised and unsupervised learning. Image by author.

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AWS Machine Learning Blog

JUNE 3, 2025

Bevar Ukraine was established in 2014 and has been at the forefront of supporting Ukrainian refugees in Denmark since the full-scale war in 2022, providing assistance to over 30,000 Ukrainians with housing, job search, and integration services. In the process of implementation, we discovered that Anthropics Claude 3.5

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Dataconomy

MAY 16, 2023

These tools use advanced natural language processing (NLP) and deep learning algorithms to analyze your input, generate relevant suggestions, and improve your writing style. This software uses natural language processing techniques to rapidly and accurately summarize lengthy articles or documents.

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Flipboard

MARCH 9, 2023

For more information about multi-GPU hosting, refer to How Mantium achieves low-latency GPT-J inference with DeepSpeed on Amazon SageMaker and Deploy BLOOM-176B and OPT-30B on Amazon SageMaker with large model inference Deep Learning Containers and DeepSpeed. We discuss more considerations on concurrency in the next section.

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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 ). VAEs can generate new samples from the learned latent distribution, making them ideal for image generation and style transfer tasks.

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Dataconomy

FEBRUARY 6, 2024

These companies are using AI and ML to improve existing processes, reduce risks, and predict business performance and industry trends. When it comes to the role of AI in information technology, machine learning, with its deep learning capabilities, is the best use case. times since 2017.

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Dataconomy

JULY 12, 2023

The ChatGPT language model, which is supported by the GPT-3.5 It is able to comprehend the context and deliver responses that are human-like thanks to its natural language processing abilities. At the Google Play event honoring the top apps and games of 2022, it was named Best Overall App.

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Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system.

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ODSC - Open Data Science

APRIL 3, 2023

Question Answering is the task in Natural Language Processing that involves answering questions posed in natural language. In 2022, she received an ERC Advanced Grant to support her vision for the next big step in NLP, “InterText — Modeling Text as a Living Object in a Cross-Document Context.”

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Becoming Human

MARCH 16, 2023

Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. Machine Learning Machine learning (ML) focuses on training computer algorithms to learn from data and improve their performance, without being explicitly programmed. A few AI technologies are empowering drug design.

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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. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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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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Applied Data Science

AUGUST 2, 2021

Big Ideas What to look out for in 2022 1. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Automation Automating data pipelines and models ➡️ 6.

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Google Research AI blog

MARCH 7, 2023

books, magazines, newspapers, forms, street signs, restaurant menus) so that they can be indexed, searched, translated, and further processed by state-of-the-art natural language processing techniques. These OCR products digitize and democratize the valuable information that is stored in paper or image-based sources (e.g.,

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The MLOps Blog

MARCH 23, 2023

The size of large NLP models is increasing | Source Such large natural language processing models require significant computational power and memory, which is often the leading cause of high infrastructure costs. 2022 where they show how to train a model on a fixed-compute budget. 2020 or Hoffman et al.,

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Pickl AI

JULY 24, 2024

” During this time, researchers made remarkable strides in natural language processing, robotics, and expert systems. Notable achievements included the development of ELIZA, an early natural language processing program created by Joseph Weizenbaum, which simulated human conversation.

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IBM Journey to AI blog

JUNE 26, 2023

Generative AI uses an advanced form of machine learning algorithms that takes users prompts and uses natural language processing (NLP) to generate answers to almost any question asked. in 2022 and it is expected to be hit around USD 118.06 It’s like having a conversation with a very smart machine.

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Mlearning.ai

FEBRUARY 9, 2023

AI tools, such as ChatGPT and DALL-E, are developed with deep learning techniques. Deep learning is a subfield of AI that aims to extract knowledge from data through complex neural networks. Performing deep learning projects is difficult. Building a deep learning model takes both money and time.

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IBM Journey to AI blog

MARCH 27, 2024

A 2022 CDP study found that for companies that report to CDP, emissions occurring in their supply chain represent an average of 11.4x In recent years, remarkable strides have been achieved in crafting extensive foundation language models for natural language processing (NLP). This is where LLMs come into play.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

Instruction fine-tuning Instruction tuning is a technique that involves fine-tuning a language model on a collection of natural language processing (NLP) tasks using instructions. He focuses on developing scalable machine learning algorithms. In this section, we provide examples of two types of fine-tuning.

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