Flipboard

JUNE 13, 2023

LOVO makes explainer videos, podcasts, social media content, and e-learning materials easy. It has over 50 templates for SEO-friendly blogs, LinkedIn posts, compelling stories, Instagram captions, Twitter tweets, and presentation slides. Deep convolutional neural network-based image super-resolution is used.

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Chatbots Life

OCTOBER 9, 2023

A weave is composed of a podcast, a newsletter summary, and a blog post, all created by your Tailor A.I. Researchers are using microwave imaging and convolutional neural networks for breast cancer screening with high accuracy in classifying profiles as healthy or diseased. ? We are looking for beta users. And it’s free!

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Heartbeat

AUGUST 15, 2023

Advanced techniques have been devised to address these challenges, such as deep learning, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). These include deep learning, convolutional neural network, recurrent neural network, data augmentation, and transfer learning.

Python 52

Python Convolutional Neural Networks Neural Network Deep Learning 52

AWS Machine Learning Blog

DECEMBER 7, 2023

Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section. CNN-QR is a proprietary ML algorithm developed by Amazon for forecasting scalar (one-dimensional) time series using causal Convolutional Neural Networks (CNNs).

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Heartbeat

NOVEMBER 27, 2023

Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs. Deep learning automates and improves medical picture analysis.

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Heartbeat

DECEMBER 8, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network.

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Heartbeat

AUGUST 14, 2023

Deep Learning-based Segmentation: Deep learning-based segmentation is a more recent technique that has gained popularity in recent years, particularly with the advent of convolutional neural networks (CNNs). CNNs can learn to segment images by training on large datasets of labeled images.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Heartbeat

MAY 30, 2023

A GAN’s generator network is usually composed of multiple layers of convolutional and deconvolutional neural networks, while the discriminator network also employs convolutional neural networks to learn to differentiate between real and generated samples.

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Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

Heartbeat

MAY 22, 2023

Schematic diagram of the overall framework of Emotion Recognition System [ Source ] The models that are used for AI emotion recognition can be based on linear models like Support Vector Machines (SVMs) or non-linear models like Convolutional Neural Networks (CNNs). We pay our contributors, and we don’t sell ads.

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Heartbeat

OCTOBER 25, 2023

d) Interpretability and Explainability: With the increasing complexity of bioinformatics models, the interpretability and explainability of their predictions have become crucial. Developing methods for model interpretability and explainability is an active area of research in bioinformatics.

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Mlearning.ai

MARCH 30, 2023

Today in this blog we will learn about transfer learning and how can you implement it using TensorFlow. To train a machine learning model or a neural network that can yield the best results requires what? Let me explain this in simple words. I hope you guys got to learn something new and enjoyed this blog.

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Neural Network AI Developer AI Development Convolutional Neural Networks 52

Heartbeat

JULY 24, 2023

Overview of Neural Style Transfer Neural style transfer is a technology that blends two images’ content and style to produce a new image incorporating both features. In order to extract the content and stylistic features from the input photos, a pre-trained convolutional neural network (CNN) is used in the process.

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Viso.ai

APRIL 29, 2024

STNs are used to “teach” neural networks how to perform spatial transformations on input data to improve spatial invariance. Spatial Transformer Networks Explained The central component of the STN is the spatial transformer module. What’s Next for Spatial Transformer Networks?

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Neural Network Computer Vision Robotics Convolutional Neural Networks 52

Google Research AI blog

APRIL 25, 2023

Just like the architecture for a neural network, the search space for the perfect burger follows a layerwise pattern, where each layer has several options with different changes to costs and performance. for the convolution layer. This simplified model illustrates a common approach for setting up search spaces.

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Neural Network Convolutional Neural Networks Algorithm Software Engineer 107

Heartbeat

JUNE 6, 2023

GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data. A GCN consists of multiple layers, each of which applies a graph convolution operation to the input graph. We pay our contributors, and we don’t sell ads.

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NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Heartbeat

JULY 6, 2023

Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs). Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Pickl AI

JULY 14, 2023

The following blog will emphasise on what the future of AI looks like in the next 5 years. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable.

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Google Research AI blog

FEBRUARY 23, 2023

Previous work had shown that convolutional neural networks (CNNs) could interpret ultrasounds acquired by trained sonographers using a standardized acquisition protocol. the LLM would generate a response explaining that yes, stress can cause nosebleeds, and detail some possible mechanisms.

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Heartbeat

MARCH 5, 2024

The primary goal of image augmentation is to improve the robustness and generalization of machine learning models, particularly convolutional neural networks (CNNs), when training on a limited amount of data. We pay our contributors, and we don't sell ads.

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Computer Vision Deep Learning Convolutional Neural Networks Machine Learning 52

Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. We pay our contributors, and we don’t sell ads. If you’d like to contribute, head on over to our call for contributors.

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Computer Vision Robotics Deep Learning Algorithm 97

Heartbeat

JANUARY 15, 2024

Convolutional Neural Networks (CNNs) have enthralled audiences with their prodigious capacity to extract nuanced patterns and features from massive volumes of data. CNNs orchestrate a symphony of convolutions, pooling, and activations to create representations that reveal the essence of the visual world.

Deep Learning 52

Deep Learning Computer Vision Neural Network Convolutional Neural Networks 52

Heartbeat

APRIL 11, 2023

The core of our model will be a pre-trained convolutional neural network (CNN) named VGG16. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Computer Vision Convolutional Neural Networks Deep Learning Machine Learning 52

Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. In this post, we will look at deep learning approaches for time series analysis and how they might be used in real-world applications. We pay our contributors, and we don’t sell ads.

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Deep Learning Neural Network Convolutional Neural Networks Python 52

Heartbeat

MARCH 28, 2023

After finishing the project, our app will look like this: Image Classification App (Video by Author) Introduction Before diving into the project, let me explain the libraries I’m going to use in this analysis. To follow along with the code in this blog, you can access the notebook I used in this project here.

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Deep Learning ML Convolutional Neural Networks Machine Learning 52

PyImageSearch

NOVEMBER 13, 2023

You’ll typically find IoU and mAP used to evaluate the performance of HOG + Linear SVM detectors ( Dalal and Triggs, 2005 ), Convolutional Neural Network methods, such as Faster R-CNN ( Girshick et al., Today, we would typically swap in a deeper, more accurate base network, such as ResNet ( He et al., 2015 ; He et al.,

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Convolutional Neural Networks Neural Network Computer Vision Deep Learning 120

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Heartbeat

JUNE 27, 2023

Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs. Similarly, RNNs can be applied to graph structures where each node is represented by a word.

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Neural Network Convolutional Neural Networks Auto-classification Deep Learning 59

Heartbeat

MAY 2, 2023

To train a neural network you give the network a set of data and it compares the output with the desired output, the difference between the two will be known as an error which will be used in adjusting the weights till the outputs align. We pay our contributors, and we don’t sell ads.

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Deep Learning Convolutional Neural Networks Algorithm Neural Network 52

Heartbeat

SEPTEMBER 20, 2023

Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately. These algorithms can learn and extract intricate features from input images by using convolutional layers.

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The MLOps Blog

FEBRUARY 22, 2024

To make the model learn new data without forgetting the past, they use techniques like knowledge distillation, loss function modification, selection of parameters that should (or should not) be updated, or just a simple regularization (which explains the name).

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PyImageSearch

JUNE 26, 2023

In the first blog post of this series, we understood why and how DETR was born, its key features, and how it differs from previous models. In the previous blog post of this series, we looked at some methodologies used in DETR, specifically Set Prediction Loss. Prediction Heads: Feed-Forward Network ➡️? That’s not the case.

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The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. DFF identifies regions in the network’s feature space that belong to the same semantic concept.

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Heartbeat

NOVEMBER 21, 2023

One of the most significant breakthroughs in this field is the convolutional neural network (CNN). In stark contrast, deep learning algorithms take a radically different approach, particularly convolutional neural networks (CNNs). We pay our contributors, and we don’t sell ads.

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Computer Vision Deep Learning NLP Neural Network 52

Heartbeat

AUGUST 29, 2023

Types of Deep Learning Approaches A variety of methods and designs are used to train neural networks under the umbrella of deep learning. Some of the symbolic approaches of deep learning are listed below: CNNs (Convolutional Neural Networks) : CNNs are frequently employed in image and video recognition jobs.

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Software Development Deep Learning Neural Network Convolutional Neural Networks 52

Heartbeat

SEPTEMBER 25, 2023

Convolutional Neural Networks (CNNs), a machine learning algorithm, has successfully identified and classified abnormalities, lesions, tumors, and other critical features in medical images such as X-rays, MRIs, CT scans, and mammograms. We pay our contributors, and we don't sell ads.

Machine Learning 52

Machine Learning Algorithm Data Scientist Automation 52

John Snow Labs

APRIL 30, 2024

It leverages the CascadeTabNet architecture, which includes the Cascade mask Region-based Convolutional Neural Network High-Resolution Network (Cascade mask R-CNN HRNet). Let me explain what is happening in the pipeline. The ImageTableDetector is a deep-learning model that identifies tables within images.

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Deep Learning NLP Convolutional Neural Networks Data Extraction 52

Marktechpost

JUNE 27, 2023

Raw Shorts To assist organizations in making explainer films, animations, and promotional movies for the web and social media, Raw Shorts provides a text-to-video creator and a video editor driven by artificial intelligence. ’s automation of turning blog posts into videos or Zoom recordings into compelling clips.

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