Flipboard

JUNE 20, 2023

Deep learning Deep learning is a specific type of machine learning used in the most powerful AI systems. It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data.

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Towards AI

JULY 19, 2023

Deep learning algorithms can be applied to solving many challenging problems in image classification. Therefore, Now we conquer this problem of detecting the cracks using image processing methods, deep learning algorithms, and Computer Vision. 196–210, 2015. irregular illuminated conditions, shading, and blemishes.

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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.,

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Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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Towards AI

FEBRUARY 3, 2024

PLoS ONE 10(7), e0130140 (2015) [2] Montavon, G., eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. .: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. Lapuschkin, S., Müller, KR. Layer-Wise Relevance Propagation: An Overview. In: Samek, W.,

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Towards AI

FEBRUARY 27, 2024

The whole machine learning industry since the early days was growing on open source solutions like scikit learn (2007) and then deep learning frameworks — TensorFlow (2015) and PyTorch (2016). More than 99% of Fortune 500 companies use open-source code [2].

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Pickl AI

FEBRUARY 14, 2023

Significantly, by leveraging technologies like deep learning and proprietary algorithms for analytics, Artivatic.ai Arya.ai One of the growing AI companies in India, Arya.ai, deploys Deep Learning solutions for the BFSI sector. launched its meta framework on TensorFlow in 2015. Artivatic.ai Artivatic.ai

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Towards AI

AUGUST 11, 2023

Development in Deep Learning was at its golden state. A small startup named OpenAI got formed then after a year, in Dec 2015. Facebook then, on the other hand, was creating a system that could predict if two picture showed the same person.

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Flipboard

AUGUST 25, 2023

Zhavoronkov has a narrower definition of AI drug discovery, saying it refers specifically to the application of deep learning and generative learning in the drug discovery space. The “deep learning revolution” — a time when development and use of the technology exploded — took off around 2014, Zhavoronkov said.

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Towards AI

FEBRUARY 14, 2023

OpenAI, on the other hand, is an AI research laboratory that was founded in 2015. The first step is to learn the basics of machine learning and deep learning, which are the technologies that underpin generative AI. How to Get Started With Generative AI?

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Iguazio

MARCH 1, 2024

Here are 10 excellent open manufacturing datasets and data sources for manufacturing data for machine learning. The dataset’s base year is 2015 and depicts monthly growth rates. To learn more about ML and manufacturing, click here. Get the dataset here.

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Data Science Dojo

SEPTEMBER 13, 2023

Deep learning And NLP Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information.

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ML Review

FEBRUARY 26, 2018

Launched in July 2015, AliMe is an IHCI-based shopping guide and assistant for e-commerce that overhauls traditional services, and improves the online user experience. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL). 5] Mnih V, Badia A P, Mirza M, et al.

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NYU Center for Data Science

SEPTEMBER 28, 2023

Cho’s work on building attention mechanisms within deep learning models has been seminal in the field. Research Highlights He, Linzen, and Sedoc provided examples of the diversity of research focuses at ML².

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Source: Robust Physical-World Attacks on Deep Learning Visual Classification. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Robust Physical-World Attacks on Deep Learning Visual Classification.

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Heartbeat

APRIL 23, 2023

SpaCy is a popular open-source NLP library developed in 2015 by Matthew Honnibal and Ines Montani, the founders of the software company Explosion. Developing a chatbot can be a complex task, but with the help of SpaCy, it can be made easier. In this article, we will explore how to develop a chatbot using the SpaCy NLP library. What is SpaCy?

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Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. It consists of 16 layers, all of which are convolutional or fully connected layers.

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Pickl AI

APRIL 8, 2024

Lack of Explainability Many AI systems, particularly deep learning models, known for their “black box” nature. Example In 2015, a team at Google developed an AI system designed to detect diabetic retinopathy from retinal images.

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IBM Journey to AI blog

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

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DataRobot

MAY 13, 2021

In 2015 he joined Branded Entertainment Network (BEN), where he is now CEO (BEN took over Plaid). Using deep learning and neural networks, BEN can make sense of the billions of videos and images out there and predict where marketers can catch an emerging trend. But there is much more out there, Ricky Ray says.

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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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AWS Machine Learning Blog

OCTOBER 9, 2023

He has 8 years of experience building out a variety of deep learning and other AI use cases and focuses on Personalization and Recommendation use cases with AWS. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. References [1] Maxwell Harper and Joseph A. DOI= [link]

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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., 2015; Huang et al., an image) with the intention of causing a machine learning model to misclassify it (Goodfellow et al., 2012; Otsu, 1979; Long et al.,

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AWS Machine Learning Blog

FEBRUARY 24, 2023

This dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2019. Amazon Rekognition makes it easy to add image and video analysis into our applications, using proven, highly scalable, deep learning technology.

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Flipboard

JULY 31, 2023

Object detection works by using machine learning or deep learning models that learn from many examples of images with objects and their labels. In the early days of machine learning, this was often done manually, with researchers defining features (e.g., Object detection is useful for many applications (e.g.,

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Heartbeat

DECEMBER 20, 2023

Understanding why ResNet is essential, its innovative aspects, and what it enables in deep learning forms a crucial part of our exploration. This comparison is academic and practical, offering deep learning practitioners insights into choosing the right architecture based on specific project needs.

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Flipboard

SEPTEMBER 4, 2023

Container runtimes are consistent, meaning they would work precisely the same whether you’re on a Dell laptop with an AMD CPU, a top-notch MacBook Pro , or an old Intel Lenovo ThinkPad from 2015. These images also support interfacing with the GPU, meaning you can leverage it for training your Deep Learning networks written in TensorFlow.

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. December 14, 2015. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition.” [link] [4] Huh, Minyoung, Pulkit Agrawal, and Alexei A. April 14, 2015.

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PyImageSearch

JULY 3, 2023

Figure 4: The Netflix personalized home page generation problem (source: Alvino and Basilico, “Learning a Personalized Homepage,” Netflix Technology Blog , 2015 ). 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.

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Explosion

SEPTEMBER 3, 2015

The new, awesome deep-learning model is there, but so are lots of others. Ideally there would be a date, but it’s still obvious that this isn’t software anyone should be executing in 2015, unless they’re investigating the history of the field. When it performs little better than chance, you can’t even tell from its output.

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Dataconomy

NOVEMBER 20, 2023

Co-inventing AlexNet with Krizhevsky and Hinton, he laid the groundwork for modern deep learning. His work on the sequence-to-sequence learning algorithm and contributions to TensorFlow underscore his commitment to pushing AI’s boundaries. But, in 2015, he takes a leap of faith, leaving Google to co-found OpenAI.

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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool. Bowman et al.

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Most of its products use machine learning or deep learning models for some or all of their features.

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DagsHub

FEBRUARY 8, 2024

The advancements in deep learning have resulted in exceptional precision rates for object detection. 2) Deep learning algorithms with two stages, including examples such as various R-CNN models faster object separation from the background with faster speeds and higher accuracy. Sercan Çayır et al.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. Interestingly, “to ghost” wasn’t very common in 2015.

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Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. It also allows distributed training.

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PyImageSearch

NOVEMBER 6, 2023

This configuration ensures that our model is trained efficiently and effectively, leveraging the best practices in deep learning. The torch library is essential for our deep learning tasks, while the Dataset class from torch.utils.data provides a template for creating custom datasets ( Lines 7 and 9 ).

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