Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. The birth of Neural networks was initiated with an approach akin to structuring solving problems with algorithms modeled after the human brain.

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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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Becoming Human

MARCH 17, 2023

Amazon Alexa was launched in 2014 and functions as a household assistant. What Challenges and Innovations Lie Ahead in the Future for Virtual Assistants In a nutshell, most virtual assistants work with deep neural networks focusing on not just finding the right answer to a query but also feasibly converting text and voice back and forth.

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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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. We found a few engineers and spent almost six months building our first model, a neural network that barely worked and had about 128 million parameters,” an often-used measure of an AI model’s capability.

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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 ). Understanding biological neural networks is one current focus. 💥 Miscellaneous – a set of rapid-fire questions What is your favorite area of research outside of generative AI?

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Mlearning.ai

APRIL 1, 2023

Word2Vec is a shallow neural network that learns to predict the probability of a word given its context (CBOW) or the context given a word (skip-gram). The context words are the input to the neural network, and the centre word is the output. Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov.

Deep Learning 52

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Dlabs.ai

AUGUST 20, 2020

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. Then, show you how to build a deep neural network from scratch.

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Topbots

MARCH 5, 2024

A Brief Look Into the History of Creative AI Generative Adversarial Networks (GANs) for image generation were introduced in 2014. In 2015, engineer and creative storyteller Samim trained a neural network on 14 million lines of passages from romance novels and asked the model to generate original stories based on new images.

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Heartbeat

FEBRUARY 27, 2023

DRL agents may now be trained on high-dimensional observations like images and videos thanks to the usage of deep neural networks as function approximators. A significant advancement in DRL has been the introduction of new continuous action space handling algorithms like DDPG and TD3.

Machine Learning 52

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Dlabs.ai

JANUARY 12, 2021

Development started in 2014. Still, over the years, it has proved a reliable library for building neural networks. W3C is considering standardizing the Neural Network Web API — which would, in turn, make machine learning accessible to a broader user base and resolve the three issues. But there’s one exception.

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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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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. Throughout the 2000s , pharmaceutical giants and plucky startups saw an opportunity to accelerate the drug development process.

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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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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Generative AI Generative AI is another crucial skill for the role of prompt engineering, as it encompasses the core ability to leverage AI to create new content, whether it be text, images, or other forms of media.

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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). The main CV methods include image classification, image localization, object detection, and segmentation. Find the SURF paper here. Find the ImageNet paper here.

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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). In the original paper, it is set to 0.3.

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Marktechpost

DECEMBER 6, 2023

This limits the amount of interaction when utilizing the generation pipeline as a creative tool, usually requiring tens to hundreds of expensive neural network evaluations. on MS-COCO 2014-30k using the same denoiser architecture as Stable Diffusion. DMD obtains a competitive FID of 11.49

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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. 2015 ; He et al.,

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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. Optimization of drug dosing and treatment regimens Predictive modeling of patient responses to treatment Deep Learning Deep Learning (DL) is a subset of ML based on using artificial neural networks (ANNs).

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Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

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ODSC - Open Data Science

APRIL 28, 2023

Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. Convolutional Neural Network for sentiment analysis A CNN model is a type of neural architecture that is based on learned matrices of numbers (filters) that slide (convolve) over the input data.

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Mlearning.ai

MARCH 9, 2023

Fully convolutional networks for semantic segmentation. Neural Networks, 64, 59–63. Intriguing properties of neural networks. A simple guide for defending deep neural networks. 2013; Goodfellow et al., Contour detection and hierarchical image segmentation. Goodfellow, I. Shlens, J., & Szegedy, C.

Computer Vision 52

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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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AssemblyAI

AUGUST 15, 2023

**The history of asynchronous I/O in Python** In the late 1990s and early 2000s, the Python standard library included modules for asynchronous I/O and networking. Around 2012 to 2014, developers proposed updating these modules, but were told to use third party libraries instead. However, over time these modules became outdated.

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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

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Viso.ai

DECEMBER 3, 2023

Today, the most powerful image processing models are based on convolutional neural networks (CNNs). A popular library that uses neural networks for real-time human pose estimation in 3D, even for multi-person use cases, is named OpenPose. High-Resolution Net (HRNet) is a neural network for human pose estimation.

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Mlearning.ai

JUNE 16, 2023

Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks.” February 23, 2014. pre-training). fine-tuning). January 29, 2015.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. 21 million ops) gets reduced by a factor of ~11.

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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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Mlearning.ai

JUNE 8, 2023

Images can be embedded using models such as convolutional neural networks (CNNs) , Examples of CNNs include VGG , and Inception. The Unreasonable Effectiveness Of Neural Network Embeddings An embedding is a low-dimensional vector representation that captures relationships in higher dimensional input data.

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John Snow Labs

MAY 23, 2023

The first VQA dataset was DAQUAR, released in 2014. VQA frameworks combine two Deep Learning architectures to deliver the final answer: Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Network (RNN) (and its special variant Long Short Term Memory networks or LSTM) for NLP processing.

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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

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Viso.ai

DECEMBER 6, 2023

State of Computer Vision Tasks in 2024 The field of computer vision today involves advanced AI algorithms and architectures, such as convolutional neural networks (CNNs) and vision transformers ( ViTs ), to process, analyze, and extract relevant patterns from visual data. Get a demo here.

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Heartbeat

APRIL 18, 2023

The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. It was introduced in 2014 by a group of researchers (A. Zisserman and K. Simonyan) from the University of Oxford.

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Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). The last Dense layer is the network’s output layer, it takes in a variable shape which can be either 4 or 3, denoting the number of classes for therapist and client. Introduction In natural language processing, text categorization tasks are common (NLP). As a technical writer, every little bit helps. Dönicke, T.,

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deepsense.ai

MAY 7, 2023

Next, we embed the images using an Inception-based [ 5 ] neural network. This solution is based on several Convolutional Neural Networks that work in a cascade fashion to locate the face with some landmarks in an image. 2014 Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks , Zhang et al.

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