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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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% ).

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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. We will discuss each of these architectures in detail.

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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.

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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. GANs consist of two neural networks, a generator & a discriminator, which contest in a zero-sum game.

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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.

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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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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. 2003) “ Support-vector networks ” by Cortes and Vapnik (1995) Significant people : David Blei Corinna Cortes Vladimir Vapnik 4.

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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.

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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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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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AWS Machine Learning Blog

AUGUST 2, 2024

We also released a comprehensive study of co-training language models (LM) and graph neural networks (GNN) for large graphs with rich text features using the Microsoft Academic Graph (MAG) dataset from our KDD 2024 paper. in computer systems and architecture at the Fudan University, Shanghai, in 2014. He received his Ph.D.

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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. The more layers of interconnected neurons a neural network has, the more “deep” it is.

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Viso.ai

JUNE 26, 2024

The Concept of Neuromorphic Engineering [ Source ] Core Principles of Neuromorphic Engineering The core principle of neuromorphic engineering is to develop models that replicate the working mechanism of biological neural networks and process information just like a human brain does.

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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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Viso.ai

JUNE 19, 2024

AlphaPose is a multi-person pose estimation model that uses computer vision and deep learning techniques to detect and predict human poses from images and videos in real time. History of Pose Estimation Before the start of deep learning, pose estimation relied on manual techniques, where humans did a lot of work.

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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.

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Dlabs.ai

AUGUST 20, 2020

If you’re looking for the best free eBooks related to artificial intelligence, machine learning, or deep learning – this list is for you. Dive into Deep Learning Authors: Aston Zhang, Zack C. Smola The first eBook on our must-read list is a deep-dive into deep learning.

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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. Here is the journey of NST. Gatys et al. 2015) The research paper by Leon A.

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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. 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.

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Heartbeat

FEBRUARY 27, 2023

Photo by Markus Spiske on Unsplash Deep learning has grown in importance as a focus of artificial intelligence research and development in recent years. Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends.

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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. 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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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

MAY 31, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. provides a robust end-to-end computer vision infrastructure – Viso Suite. Get a demo here.

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Viso.ai

DECEMBER 6, 2023

Our solution enables leading companies to use a variety of machine learning models and tasks for their computer vision systems. The most common example is security analytics , where deep learning models analyze CCTV footage to detect theft, traffic violations, or intrusions in real-time. Get a demo here.

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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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Viso.ai

JULY 9, 2024

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. These two networks compete against each other in a zero-sum game.

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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. Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. February 23, 2014. pre-training). fine-tuning).

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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. I don't have a favorite area, there are a ton of research problems I'm interested in and I've historically worked across many areas.

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PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Before the rise of GANs, there were other foundational neural network architectures for generative modeling.

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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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deepsense.ai

MAY 7, 2023

We talked about diffusion in deep learning, models that utilize it to generate images, and several ways of fine-tuning it to customize your generative model. All of that can leave even the toughest deep-learning practitioner confused. Next, we embed the images using an Inception-based [ 5 ] neural network.

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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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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. On large-batch training for deep learning: Generalization gap and sharp minima.” Toward understanding the impact of staleness in distributed machine learning.”

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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. Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn.

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Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. It is well understood that the more data a machine learning algorithm has, the more effective it may be. Ensemble deep learning: A review.

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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. Some of the research areas that are being explored include Deep Learning, Sensor Fusion, Transfer Learning, Context-aware etc.

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