ODSC - Open Data Science

APRIL 28, 2023

In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. deep” architecture). His major focus has been on Natural Language Processing (NLP) technology and applications.

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Allen AI

JUNE 20, 2023

Founded in 2014, AI2 is the research institute created by the late philanthropist Paul G. While at AI2, Farhadi co-founded Xnor.ai, the first on-device Deep Learning startup that was acquired by Apple in 2020. Allen , co-founder of Microsoft, to drive high-impact AI research and engineering. Brainchild of the late Paul G.

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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.

Neural Network 156

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Unite.AI

JANUARY 9, 2024

Google focuses on expanding AI in search, advertising, cloud, healthcare, and education, with a particular emphasis on deep learning. Similarly, Google's acquisition of DeepMind in 2014 improved services like search and recommendations. Moreover, some acquisitions have led to legal disputes and financial consequences.

Natural Language Processing 147

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Towards AI

APRIL 23, 2024

However, generative models is not a new term and it has come a long way since Generative Adversarial Network (GAN) was published in 2014 [1]. It is one of the first algorithms to combine images based on deep learning. Neural Style Transfer (NST) was born in 2015 [2], slightly later than GAN.

Computer Vision 111

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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. 180–194, 2014. A4014004, 2014. Golparvar-Fard, and K.

Computer Vision 98

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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. Introduction Recurrent Neural Networks (RNNs) are a cornerstone of Deep Learning. With the global Deep Learning market projected to grow from USD 49.6

Deep Learning 52

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AssemblyAI

SEPTEMBER 12, 2023

Though once the industry standard, accuracy of these classical models had plateaued in recent years, opening the door for new approaches powered by advanced Deep Learning technology that’s also been behind the progress in other fields such as self-driving cars. The data does not need to be force-aligned.

Deep Learning 130

Deep Learning Machine Learning Categorization Data Analysis 130

Marktechpost

MAY 23, 2024

Generative Adversarial Networks: Creating Realistic Synthetic Data Generative Adversarial Networks, introduced by Ian Goodfellow in 2014, are a class of machine-learning frameworks designed for generative tasks. Finance: RL models optimize strategies for buying and selling assets to maximize returns in trading.

Artificial Intelligence 125

Artificial Intelligence Artificial Intelligence Neural Network Robotics 125

Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

Deep Learning 52

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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.

Machine Learning 98

Machine Learning Software Engineer Neural Network Data Science 98

Lexalytics

JULY 12, 2023

We’ve pioneered a number of industry firsts, including the first commercial sentiment analysis engine, the first Twitter/microblog-specific text analytics in 2010, the first semantic understanding based on Wikipedia in 2011, and the first unsupervised machine learning model for syntax analysis in 2014.

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Unite.AI

JUNE 11, 2024

It was in 2014 when ICML organized the first AutoML workshop that AutoML gained the attention of ML developers. Third, the NLP Preset is capable of combining tabular data with NLP or Natural Language Processing tools including pre-trained deep learning models and specific feature extractors.

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Marktechpost

SEPTEMBER 14, 2024

Recent advances, however, have utilized deep learning to analyze large climate datasets and uncover complex patterns. Previous D&A studies have varied in methodology, with approaches including PCA analysis, regression, and machine learning models to identify climate fingerprints and assess warming trends.

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Viso.ai

OCTOBER 9, 2024

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Recent advances in supervised and unsupervised machine learning techniques brought breakthroughs in the research field, and more and more accurate systems are emerging every year.

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Convolutional Neural Networks Neural Network Computer Vision Emotion AI 98

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% ).

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Mlearning.ai

APRIL 1, 2023

Doc2Vec Doc2Vec, also known as Paragraph Vector, is an extension of Word2Vec that learns vector representations of documents rather than words. Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov. Doc2Vec learns vector representations of documents by combining the word vectors with a document-level vector.

Deep Learning 52

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. AI drug discovery is exploding. A few AI technologies are empowering drug design.

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Towards AI

AUGUST 11, 2023

The Trademark of GPT-5 In a 2014 BBC interview, Stephen Hawking said the following words – The development of full artificial intelligence could spell the end of the human race. The state of AI in 2014 was different from today. In that year, Google bought DeepMind — a machine learning startup — for over $600 Million.

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Towards AI

AUGUST 11, 2023

GANs are a part of the deep-learning world and were very introduced by Ian Goodfellow and his collaborators in 2014, After that GANs have rapidly captivated many researchers’ eyes which resulted in much research and also helped to redefine the boundaries of creativity and artificial intelligence in the world of AI 1.1

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Marktechpost

OCTOBER 16, 2024

Deep learning-based prediction is critical for optimizing output, anticipating weather fluctuations, and improving solar system efficiency, allowing for more intelligent energy network management. More sophisticated machine learning approaches, such as artificial neural networks (ANNs), may detect complex relationships in data.

Machine Learning 65

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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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Towards AI

DECEMBER 9, 2024

In deep learning, we have studied various types of RNN structures i.e. One to One, Many to One, One to Many and Many to Many. Last Updated on December 10, 2024 by Editorial Team Author(s): Navdeep Sharma Originally published on Towards AI. The brains behind modern AI: Exploring the evolution of Large Language Models.

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role. Is aqua the maximum?

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

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. 2014) Significant people : Geoffrey Hinton Yoshua Bengio Ilya Sutskever 5.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

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.

Machine Learning 123

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Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. The output layer is set to use Softmax Activation Function as usual in Deep Learning classifiers. That time, tensorflow/pytorch and the Deep Learning technology were not ready yet.

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Viso.ai

MARCH 12, 2024

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here. Find the ImageNet paper here.

Computer Vision 52

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NVIDIA

SEPTEMBER 6, 2023

Pioneering AI in Physics In 2014, her life’s work took her more than 7,000 miles from her Shanghai home to Princeton University’s prestigious plasma physics lab, where she earned a Ph.D. “She said clean energy was really important for sustaining humanity, she talked about it a lot,” said Ge Dong (above at age two with her mom).

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NVIDIA

NOVEMBER 3, 2023

Machine learning can be trained to recognize DDoS attack patterns to prevent the type of incoming malicious traffic that brought down U.S. In 2014, the Port of LA established a cybersecurity operations center and hired a dedicated cybersecurity team. airport websites last year.

Machine Learning 126

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AWS Machine Learning Blog

AUGUST 2, 2024

About the Author Xiang Song is a senior applied scientist at AWS AI Research and Education (AIRE), where he develops deep learning frameworks including GraphStorm, DGL and DGL-KE. He is now leading the development of GraphStorm, an open-source graph machine learning framework for enterprise use cases. He received his Ph.D.

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ML Review

JUNE 4, 2018

Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

How to Learn Machine Learning

JULY 20, 2024

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. However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs).

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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., 2013; Goodfellow et al., For instance, Xu et al.

Deep Learning 52

Deep Learning Computer Vision Machine Learning Algorithm 52

NVIDIA

AUGUST 8, 2023

From Concept to Company In 2014, he shared his story with May Habib, an entrepreneur he met while working in Dubai. They agreed to create a startup that could help marketing departments — which are always pressured to do more with less — use machine learning to quickly create copy for their web pages, blogs, ads and more.

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Snorkel AI

JANUARY 10, 2023

And, in 2014, deep learning came into play addressing some of these challenges, and it revolutionized the way ASR is done. Firstly, the quality is enough that companies were able to deploy deep-learning ASR in production. Then, this is deep-learning-based text-to-speech.

Deep Learning 52

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Viso.ai

MAY 7, 2024

GoogLeNet, released in 2014, set a new benchmark in object classification and detection through its innovative approach (achieving a top-5 error rate of 6.7%, nearly half the error rate of the previous year’s winner ZFNet with 11.7%) in ImageNet Large Scale Visual Recognition Challenge (ILSVRC).

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