Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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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 what is the procedure?

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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. The recent deep learning algorithms provide robust person detection results. Detecting people in video streams is an important task in modern video surveillance systems.

Convolutional Neural Networks 98

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Towards AI

MARCH 20, 2023

How do neural networks contribute to generative AI? In this blog, we will explore the top most common questions related to generative AI, covering topics such as its history, neural networks, natural language processing, training, applications, ethical concerns, and the future of the technology.

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Generative AI Neural Network Natural Language Processing AI Modeling 95

Becoming Human

MARCH 17, 2023

Amazon Alexa was launched in 2014 and functions as a household assistant. Nuance , an innovation specialist focusing on conversational AI, feeds its advanced Natural Language Processing (NLU) algorithm with transcripts of chat logs to help its virtual assistant, Pathfinder, accomplish intelligent conversations.

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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. AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. AI drug discovery is exploding.

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Towards AI

NOVEMBER 8, 2024

The text-generation process involves utilizing algorithms and models to generate the written content based on the given input, for instance, it can be a prompt, a set of keywords, or even a specific context. They’re called Gated Recurrent Units, and they’re basically an upgraded type of neural network that came out in 2014.

Automation 122

Automation Natural Language Processing Neural Network NLP 122

PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different.

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AssemblyAI

AUGUST 15, 2023

**Improving CPython's performance** Guido initially coded CPython simply and efficiently, but over time more optimized algorithms were developed to improve performance. The example of prime number checking illustrates the time-space tradeoff in algorithms. **The However, over time these modules became outdated.

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Viso.ai

DECEMBER 3, 2023

This article will explore the latest advances in pose analytics algorithms and AI vision techniques, their applications and use cases, and their limitations. Today, the most powerful image processing models are based on convolutional neural networks (CNNs). Definition: What is pose estimation?

Deep Learning 98

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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.

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Viso.ai

JUNE 26, 2024

This article will discuss the following: Neuromorphic Engineering and its core principles History and Development Algorithms Used How Neuromorphic Algorithms differ from Traditional Algorithms Real-world examples Applications and Use Cases About Us: At Viso.ai, we power Viso Suite, the most complete end-to-end computer vision platform.

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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Flipboard

AUGUST 25, 2023

Deep learning is a type of machine learning where artificial neural networks ( similar but not exactly like the neurons in our brain) allow a machine to learn and advance independent of human intervention. The idea that somebody hits ‘enter’ on an AI algorithm and a drug just pops out, I believe that is a fallacy,” said Gibson. “I

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers.

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Algorithm Deep Learning Neural Network Machine Learning 95

Dlabs.ai

AUGUST 20, 2020

It explains how to make machine learning algorithms work. Deep Learning with PyTorch Authors: Eli Stevens, Luca Antiga, Thomas Viehmann If you’re planning to build neural networks with PyTorch, you’ll want to begin your journey with this popular, open-source machine learning framework. His online courses were attended by over 2.5

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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., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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Google Research AI blog

JANUARY 18, 2023

Over the next several weeks, we will discuss novel developments in research topics ranging from responsible AI to algorithms and computer systems to science, health and robotics. The neural network perceives an image, and generates a sequence of tokens for each object, which correspond to bounding boxes and class labels.

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Pickl AI

SEPTEMBER 30, 2024

Introduction Generative Adversarial Networks (GANs) have emerged as one of the most exciting advancements in the field of Artificial Intelligence and Machine Learning since their introduction in 2014 by Ian Goodfellow and his collaborators. Discriminator : This network evaluates the data produced by the generator against real data.

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Viso.ai

DECEMBER 6, 2024

After that, they utilize specialized algorithms to identify trends, predict outcomes, and absorb fresh data. It is achieved by computer vision algorithms. Generally speaking, autonomous cars use a variety of sensors in addition to advanced computer vision algorithms to gather the data they need from their surroundings.

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Heartbeat

FEBRUARY 27, 2023

Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends. Below are some of the most promising use cases for DRL and GANs: DRL: Robotics: DRL algorithms can be applied to teach robots how to carry out particular tasks, including grabbing items or navigating.

Machine Learning 52

Machine Learning Deep Learning Robotics Neural Network 52

ML Review

MARCH 5, 2019

Conducting exploratory search is difficult in standard IR systems as terminology might differ even in closely related fields (network analyses vs graph neural networks). Crafting a dataset The number of papers added to ArXiv per month since 2014. How to find similar phrases without knowing what you’re searching for?

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Viso.ai

DECEMBER 6, 2023

Modern computer vision research is producing novel algorithms for various applications, such as facial recognition, autonomous driving, annotated surgical videos, etc. For instance, CV algorithms can understand Light Detection and Ranging (LIDAR) data for enhanced perceptions of the environment. Get a demo here.

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Dlabs.ai

NOVEMBER 16, 2020

Suguard is an internal project we’ve been developing since 2014, which is when we founded DiabetesLab : our second company focused on creating advanced software that helps people manage an illness using AI. The technology uses convolutional neural networks to indicate likely issues on a patient’s retina, boasting accuracy levels of 92.3%

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Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Neural Network 52

Viso.ai

JULY 17, 2024

Background and History of Neural Style Transfer NST is an example of an image styling problem that has been in development for decades, with image analogies and texture synthesis algorithms paving foundational work for NST. Layer Reconstruction in VGG-19 network for style transfer. Here is the journey of NST. Gatys et al.

Convolutional Neural Networks 52

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. Doc2Vec: introduced in 2014, adds on to the Word2Vec model by introducing another ‘paragraph vector’. using its Spectrogram ).

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Machine Learning BERT Neural Network OpenAI 52

Viso.ai

MAY 31, 2024

Pascal VOC (which stands for Pattern Analysis, Statistical Modelling, and Computational Learning Visual Object Classes) is an open-source image dataset for a number of visual object recognition algorithms. As a result of Pascal VOC, researchers, and developers were able to compare various algorithms and methods on an entity basis.

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John Snow Labs

MAY 23, 2023

This is because NLP technology enables the VQA algorithm to not only understand the question posed to it about the input image, but also to generate an answer in a language that the user (asking the question) can easily understand. The first VQA dataset was DAQUAR, released in 2014. For example, the question “what is in the image?”

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Marek Rei

NOVEMBER 12, 2019

Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. link] Extending recurrent neural network grammars to the unsupervised setting, discovering constituency parses only from plain text. Harvard, Oxford, DeepMind. NAACL 2019. Turakhia, Andrew Y.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same.

BERT 52

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Pickl AI

NOVEMBER 28, 2024

Understanding the Basics of GANs Generative Adversarial Networks (GANs) are a class of Machine Learning models introduced by Ian Goodfellow in 2014. At their core, GANs consist of two neural networks —a Generator and a Discriminator—that compete in a game-like scenario. How Generative Adversarial Networks (GANs) Work?

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TheSequence

NOVEMBER 22, 2023

I co-authored my first AI-related paper in 2000 ( using neural networks to manage on-CPU hardware resources ). Some of that work involved deep algorithmic work, which culminated in this year’s announcement of the MX consortium , which is standardized 4, 6, and 8-bit datatypes for ultra-efficient AI computation.

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Topbots

MARCH 5, 2024

From algorithms that compose symphonies to software that drafts novels, the scope of computer-generated creativity is expanding, challenging our preconceived notions of artistry and originality. A Brief Look Into the History of Creative AI Generative Adversarial Networks (GANs) for image generation were introduced in 2014.

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PyImageSearch

OCTOBER 2, 2023

However, in the realm of unsupervised learning, generative models like Generative Adversarial Networks (GANs) have gained prominence for their ability to produce synthetic yet realistic images. Before the rise of GANs, there were other foundational neural network architectures for generative modeling. on Lines 6 and 7.

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Knowledge in these areas enables prompt engineers to understand the mechanics of language models and how to apply them effectively.

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ML Review

MAY 3, 2018

Similar to the advancements seen in Computer Vision, NLP as a field has seen a comparable influx and adoption of deep learning techniques, especially with the development of techniques such as Word Embeddings [6] and Recurrent Neural Networks (RNNs) [7]. Thus the algorithm is alignment-free. Deep Learning est en train de mourir.

Neural Network 52

Neural Network Computer Vision Deep Learning NLP 52

Heartbeat

APRIL 18, 2023

HAR systems typically use machine learning algorithms to learn and classify human actions based on the visual features extracted from the input data. The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. Human movement Gif Why is HAR important?

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