Heartbeat

MARCH 22, 2023

This week, we’ve got some fantastic pieces on NLP, including a discussion of RoBERTa, and some deep dives into computer vision. A Vision for the Future: How Computer Vision is Transforming Robotics — by Randy Barak a Computer vision is crucial to robotics because it allows robots to see and interpret their environment.

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Viso.ai

JUNE 4, 2024

In many computer vision applications, engineers gather data manually. provides a robust end-to-end computer vision infrastructure – Viso Suite. Viso Suite is the only end-to-end computer vision platform What are Point Clouds? 3D Data Representation of a Rabbit – Source About us : Viso.ai

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

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Viso.ai

JULY 9, 2024

Object detection is one of the crucial tasks in Computer Vision (CV). Computer vision researchers introduced YOLO architecture (You Only Look Once) as an object-detection algorithm in 2015. About Us: At Viso.ai, we power Viso Suite, the most complete end-to-end computer vision platform. YOLOv2 achieved 76.8

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NVIDIA

NOVEMBER 8, 2023

on RetinaNet, a computer vision model. They included Dell Technologies and supercomputing centers at Clemson University, the Texas Advanced Computing Center and — with assistance from Hewlett Packard Enterprise (HPE) — Lawrence Berkeley National Laboratory. For example, H100 GPUs were 1.6x Performance was up 1.8x

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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.

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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.

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NVIDIA

NOVEMBER 8, 2023

on RetinaNet, a computer vision model. They included Dell Technologies and supercomputing centers at Clemson University, the Texas Advanced Computing Center and — with assistance from Hewlett Packard Enterprise (HPE) — Lawrence Berkeley National Laboratory. For example, H100 GPUs were 1.6x Performance was up 1.8x

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AWS Machine Learning Blog

AUGUST 16, 2023

The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. He is interested in solving business problems with the latest AI and deep learning techniques including large language models, foundational imagery models, and generative AI.

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Metadata Data Scientist Generative AI Natural Language Processing 74

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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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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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., 2018; Sitawarin et al., 2018; Papernot et al., 2018; Papernot et al., 2018; Pang et al., 2012; Otsu, 1979; Long et al., For instance, Xu et al.

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AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). He focuses on developing scalable machine learning algorithms.

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers.

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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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Unite.AI

JANUARY 9, 2024

Apple prioritizes computer vision , natural language processing , voice recognition, and healthcare to enhance its products. Google focuses on expanding AI in search, advertising, cloud, healthcare, and education, with a particular emphasis on deep learning.

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Viso.ai

DECEMBER 3, 2023

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. Object detection is a key field in artificial intelligence, allowing computer systems to “see” their environments by detecting objects in visual images or videos.

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Viso.ai

OCTOBER 25, 2023

million seed round led by Accel to bring computer vision capabilities to companies across the globe! The investment will be used to further accelerate growing the team to serve customers worldwide, and to continue enhancing the computer vision platform’s end-to-end and no-code capabilities.

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PyImageSearch

JULY 3, 2023

Figure 3: Netflix personalized home page view (source: “NETFLIX System Design,” Medium , 2018 ). These features can be simple metadata or model-based features (extracted from a deep learning model), representing how good that video is for a member. Or requires a degree in computer science? Each row has a title (e.g.,

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The Stanford AI Lab Blog

OCTOBER 13, 2021

Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pp. In Association for Computational Linguistics (ACL), pp. 1112–1122, 2018. ↩ Yonatan Giefman and Ran El-Yaniv. SelectiveNet: A deep neural network with an integrated reject option.

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Viso.ai

MAY 20, 2023

The YOLOv7 algorithm is making big waves in the computer vision and machine learning communities. provides the only end-to-end computer vision application platform, Viso Suite. The software infrastructure is used by leading organizations to gather data, train YOLOv7 models, and deliver computer vision applications.

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Viso.ai

JULY 17, 2024

Image analogies patch-based texture in-filling for artistic rendering – source The field of Neural style transfer took a completely new turn with Deep Learning. With deep learning, the results were impressively good. 2018) StarGAN extends GANs to multi-domain image translation. Here is the journey of NST.

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Marktechpost

JULY 17, 2023

List of synthetic data startups and companies Datagen Israeli firm Datagen was founded in 2018 and has funded $22 million, including an $18.5 Consider retail automation in the shape of an Amazon Go location, where a computer vision system monitors shoppers to ensure no one leaves with any five-finger discounts.

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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. They can also perform self-supervised learning to generalize and apply their knowledge to new tasks. An open-source model, Google created BERT in 2018.

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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. At this point, datasets like […]

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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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Viso.ai

JULY 9, 2024

NVIDIA in 2018 came out with a breakthrough Model- StyleGAN, which amazed the world for its ability to generate ultra-realistic and high-quality images. StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014.

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Mlearning.ai

FEBRUARY 10, 2023

For example, if you are using regularization such as L2 regularization or dropout with your deep learning model that performs well on your hold-out-cross-validation set, then increasing the model size won’t hurt performance, it will stay the same or improve. The only drawback of using a bigger model is computational cost.

Machine Learning 52

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. ViT models outperform the current state-of-the-art (CNN) by almost x4 in terms of computational efficiency and accuracy.

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Mlearning.ai

JUNE 16, 2023

The ImageNet dataset, featuring natural images, contains 14,197,122 annotated images organized in 1000 classes and is commonly used as a benchmark for many computer vision models⁸. The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet.

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Mlearning.ai

MARCH 2, 2023

Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. Preliminaries: Transformers and Unsupervised Transfer Learning This section presents the most important theoretical background to understand BERT. BERT: Pre-training of deep bidirectional transformers for language understanding. Benchmark Results V.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. Computer Vision with NFL Player Tracking Data using torch for R: Coverage classification Using CNNs.” In Proceedings of the IEEE international conference on computer vision , pp.

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Viso.ai

DECEMBER 18, 2023

YOLOv8 is the newest model in the YOLO algorithm series – the most well-known family of object detection and classification models in the Computer Vision (CV) field. offers the world’s leading end-to-end no-code Computer Vision Platform Viso Suite. Get a demo. YOLOv2 achieved 76.8 at 22 milliseconds.

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Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

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Viso.ai

APRIL 18, 2024

Image segmentation task is a fundamental computer vision task that aims to partition a digital image into multiple segments or sets of pixels. The term “panoptic” originates from two Greek words “pan” (all) and “optic” (vision). distinguishing between multiple cars in an image).

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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. For example, Dean Pomerleau used them to create a system that learned to drive a car [ 12 ]. (I The graph below shows the trend of publications in machine learning.

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The Stanford AI Lab Blog

JANUARY 21, 2022

Deep learning has enabled improvements in the capabilities of robots on a range of problems such as grasping 1 and locomotion 2 in recent years. QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation. Conference on Robot Learning. ↩ Kumar, A., Kalashnikov, D., Quillen, D.,

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