Marktechpost

JUNE 9, 2024

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Researchers at the Institute of Biomedical Engineering, TU Dresden, developed a deep learning architecture, xECGArch, for interpretable ECG analysis.

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NVIDIA

OCTOBER 11, 2024

Deep learning — a software model that relies on billions of neurons and trillions of connections — requires immense computational power. In 2018, NVIDIA debuted GeForce RTX (20 Series) with RT Cores and Tensor Cores, designed specifically for real-time ray tracing and AI workloads.

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Marktechpost

APRIL 24, 2024

Deep reinforcement learning (Deep RL) combines reinforcement learning (RL) and deep learning. Deep RL has achieved human-level or superhuman performance for many two-player or multi-player games. 2013 DeepMind showed impressive learning results using deep RL to play Atari video games.

Neural Network 120

Neural Network Deep Learning Algorithm Robotics 120

Towards AI

APRIL 5, 2023

Picture created with Dall-E-2 Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, three computer scientists and artificial intelligence (AI) researchers, were jointly awarded the 2018 Turing Prize for their contributions to deep learning, a subfield of AI.

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Towards AI

NOVEMBER 16, 2024

In particular, min-max optimisation is curcial for GANs [2], statistics, online learning [6], deep learning, and distributed computing [7]. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXivpreprint arXiv:1706.06083, 2017.[5] Arjovsky, S. Chintala, and L. 214–223, 2017.[4] Makelov, L.

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Towards AI

DECEMBER 28, 2023

Later, Python gained momentum and surpassed all programming languages, including Java, in popularity around 2018–19. The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. CS6910/CS7015: Deep Learning Mitesh M. Khapra Homepage www.cse.iitm.ac.in

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Large Language Models Natural Language Processing Prompt Engineer Prompt Engineering 105

Marktechpost

JULY 3, 2023

We will give details of Artificial Intelligence approaches such as Machine Learning and Deep Learning. By the end of the article, you will understand how innovative Deep Learning technology leverages historical data and accurately forecasts outcomes of lengthy and expensive experimental testing or 3D simulation (CAE).

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Flipboard

MARCH 13, 2023

Yes, large language models (LLMs) hallucinate , a concept popularized by Google AI researchers in 2018. Hallucinations May Be Inherent to Large Language Models But Yann LeCun , a pioneer in deep learning and the self-supervised learning used in large language models, believes there is a more fundamental flaw that leads to hallucinations.

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Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ). Or requires a degree in computer science?

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Computer Vision Deep Learning Neural Network Machine Learning 132

Analytics Vidhya

JUNE 26, 2019

DataHack Summit 2019 Bringing Together Futurists to Achieve Super Intelligence DataHack Summit 2018 was a grand success with more than 1,000 attendees from various. The post Announcing DataHack Summit 2019 – The Biggest Artificial Intelligence and Machine Learning Conference Yet appeared first on Analytics Vidhya.

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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., 2018; Sitawarin et al., 2018; Papernot et al., 2018; Papernot et al.,

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NVIDIA

DECEMBER 4, 2023

Since its 2018 launch, MLPerf , the industry-standard benchmark for AI, has provided numbers that detail the leading performance of NVIDIA GPUs on both AI training and inference. That’s up from less than 100 million parameters for a popular LLM in 2018. GPU systems have kept pace by ganging up on the challenge.

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ODSC - Open Data Science

APRIL 17, 2023

Minor changes in the input data that are very apparent to human intelligence are not so for deep learning models. Deep learning is essentially matrix multiplication, which means even small perturbations in the coefficients can cause a significant change in the output.

Data Science 97

Data Science DevOps Deep Learning Automation 97

Unite.AI

OCTOBER 31, 2024

Amy Brown , a former healthcare executive, founded Authenticx in 2018 to help healthcare organizations unlock the potential of customer interaction data. These labels become the foundation of our AI machine learning and deep learning models.

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NVIDIA

MAY 21, 2024

RTX AI PCs — Enhanced AI for Gamers, Creators and Developers NVIDIA introduced the first PC GPUs with dedicated AI acceleration, the GeForce RTX 20 Series with Tensor Cores, along with the first widely adopted AI model to run on Windows, NVIDIA DLSS , in 2018.

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Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. A combination of simulated and real-world data was used to train the system, enabling it to generalize to new objects and tasks.

Computer Vision 97

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Mlearning.ai

MAY 5, 2023

It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape. Deep learning algorithms are designed to mimic the structure and function of the human brain, allowing them to process vast amounts of data and learn from that data over time.

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Viso.ai

DECEMBER 3, 2023

The recent deep learning algorithms provide robust person detection results. However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting ( TPR of 95%, FPR up to 0.2% ).

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NYU Center for Data Science

OCTOBER 10, 2023

“A lot happens to these interpretability artifacts during training,” said Chen, who believes that by only focusing on the end result, we might be missing out on understanding the entire journey of the model’s learning. The paper is a case study of syntax acquisition in BERT (Bidirectional Encoder Representations from Transformers).

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Viso.ai

NOVEMBER 4, 2024

Convolutional deep learning models have been successfully used to forecast weather maps, according to recent studies. Deep learning models provide near-90% accuracy for both solar and wind location prediction, outperforming other conventional ML techniques. Carballo et al.

Computer Vision 52

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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. Model as a Service MaaS provides access to pre-trained models, which are whole deep learning models that can be used for a variety of tasks.

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Natural Language Processing Deep Learning NLP ChatGPT 52

NVIDIA

SEPTEMBER 11, 2023

Overall, the results continue NVIDIA’s record of demonstrating performance leadership in AI training and inference in every round since the launch of the MLPerf benchmarks in 2018.

Computer Vision 122

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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. Yida Wang is a principal scientist in the AWS AI team of Amazon. He founded StylingAI Inc.,

Machine Learning 125

Machine Learning Big Data Deep Learning Natural Language Processing 125

NVIDIA

SEPTEMBER 12, 2023

It adapts popular deep learning techniques like backpropagation and tools like PyTorch to programming quantum computers. Xanadu designed the code to run across as many types of quantum computers as possible, so the software got traction in the quantum community soon after its introduction in a 2018 paper.

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Software Development Computer Scientist Machine Learning Deep Learning 120

The Stanford AI Lab Blog

FEBRUARY 24, 2022

2018 ), or examples which are different strong data augmentations of the same image ( Xie et al., Strong data augmentation, which applies stronger alterations to the input image than traditionally used in supervised learning, is also very useful for self-supervised contrastive learning, which we will analyze in the follow-up blog post.

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. An open-source model, Google created BERT in 2018. That’s where the foundation model enters the picture.

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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NVIDIA

DECEMBER 21, 2023

The mission is near and dear to DigitalPath employees, whose office sits not far from the town of Paradise, where California’s deadliest wildfire killed 85 people in 2018. It’s one of the main reasons we’re doing this,” said CEO Jim Higgins. “We We don’t want people to lose their lives.”

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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. Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football.

ML 88

ML Deep Learning Python Auto-classification 88

Towards AI

JULY 23, 2023

Year and work published Generative Pre-trained Transformer (GPT) In 2018, OpenAI introduced GPT, which has shown, with the implementation of pre-training, transfer learning, and proper fine-tuning, transformers can achieve state-of-the-art performance.

NLP 74

NLP Natural Language Processing Auto-classification Computer Vision 74

Pickl AI

APRIL 8, 2024

Example In 2018, a self-driving car developed by Uber struck and killed a pedestrian in Arizona. Example In 2018, it was revealed that Cambridge Analytica, a political consulting firm, had improperly harvested the personal data of millions of Facebook users without their consent.

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AI Impacts

FEBRUARY 1, 2023

The alignment problem from a deep learning perspective. link] 8 From Hacker News user api: “To give a specific example: I once wrote an objective function to train an evolving system to classify images, a simple machine learning test. "The arXiv preprint arXiv:1803.03453 (2018). link] Published November 2, 2018.

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Towards AI

JULY 4, 2024

Introduced in 2018, BERT has been a topic of interest for many, with many articles and YouTube videos attempting to break it down. Want to Learn Quantization in The Large Language Model? By Milan Tamang Quantization is a method of compressing a larger size model (LLM or any deep learning model) to a smaller size.

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Towards AI

FEBRUARY 10, 2023

China’s data center industry gets 73% of its power from coal, emitting roughly 99 million tons of CO2 in 2018 [4]. In 2018, OpenAI released an analysis showing that since 2012, the amount of computing used in the largest AI training runs has been increasing exponentially, with a doubling time of 3–4 months [8].

AI Modeling 81

AI Modeling Natural Language Processing Computer Vision OpenAI 81

NVIDIA

JUNE 28, 2023

Toronto-based Ecopia AI uses deep learning-based mapping systems to mine geospatial data, helping to produce next-generation digital maps with highly accurate segmentation of buildings, roads, forests and more. CrowdAI’s deep learning model detected buildings from this aerial image taken in the aftermath of Hurricane Michael in 2018.

ESG 52

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Mlearning.ai

JANUARY 17, 2024

The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Deep learning neural network. In the code, the complete deep learning network is represented as a matrix of weights.

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Mlearning.ai

APRIL 8, 2023

Deep Learning (Late 2000s — early 2010s) With the evolution of needing to solve more complex and non-linear tasks, The human understanding of how to model for machine learning evolved. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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