Marktechpost

NOVEMBER 16, 2023

Utilizing a two-stage convolutional neural network, the model classifies macula-centered 3D volumes from Topcon OCT images into Normal, early/intermediate AMD (iAMD), atrophic (GA), and neovascular (nAMD) stages. The study emphasizes the significance of accurate AMD staging for timely treatment initiation.

Deep Learning 108

Deep Learning Convolutional Neural Networks Neural Network Categorization 108

Towards AI

JULY 9, 2024

In this sense, it is an example of artificial intelligence that is, teaching computers to see in the same way as people do, namely by identifying and categorizing objects based on semantic categories. Another method for figuring out which category a detected object belongs to is object categorization.

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Towards AI

MAY 12, 2024

Photo by Erik Mclean on Unsplash This article uses the convolutional neural network (CNN) approach to implement a self-driving car by predicting the steering wheel angle from input images of three front cameras in the car’s center, left, and right. Levels of Autonomy. [3]

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Convolutional Neural Networks Neural Network Auto-classification Categorization 119

Unite.AI

MAY 24, 2024

Experimental results conducted to analyze the Recurrent Neural Network like mechanism of state space model conclude that the Mamba framework is suited for tasks with autoregressive or long-sequence characteristics, and is unnecessary for image classification tasks.

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Marktechpost

SEPTEMBER 12, 2023

Fine-grained image categorization delves into distinguishing closely related subclasses within a broader category. Modern algorithms for fine-grained image classification frequently rely on convolutional neural networks (CNN) and vision transformers (ViT) as their structural basis.

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Convolutional Neural Networks Categorization Neural Network Deep Learning 108

IBM Journey to AI blog

AUGUST 28, 2023

Some common techniques include the following: Sentiment analysis : Sentiment analysis categorizes data based on the nature of the opinions expressed in social media content (e.g., It also automates tasks like information extraction and content categorization. positive, negative or neutral).

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Marktechpost

JANUARY 14, 2024

The researchers present a categorization system that uses backbone networks to organize these methods. Most picture deblurring methods use paired images to train their neural networks. It employs neural networks to understand intricate feature mapping interactions to improve picture restoration quality.

Deep Learning 112

Deep Learning Convolutional Neural Networks Neural Network Computer Vision 112

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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Marktechpost

DECEMBER 24, 2023

These methods address the challenges of traditional approaches, offering more automated, accurate, and robust solutions for identifying and categorizing plant leaf diseases. As the demand for sustainable agriculture grows, machine learning emerges as a vital force, reshaping the future of food security and cultivation.

Machine Learning 116

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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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Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Marktechpost

MAY 31, 2024

These signals are essential in categorizing sleep stages and identifying sleep disorders. SleepFM employs three 1D convolutional neural networks (CNNs) to generate embeddings from each modality (BAS, ECG, and respiratory signals).

Convolutional Neural Networks 104

Convolutional Neural Networks Neural Network Automation Categorization 104

ODSC - Open Data Science

APRIL 28, 2023

This diagram I think gives you a good overview: spaCy 101: Everything you need to know Above you can see that text is processed by a “Language” object, which has a number of components such as part-of-speech tagging, vector representations, and models for categorization. These can be customized and trained. We’ll be mainly using the “.cats”

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AssemblyAI

JANUARY 23, 2025

Broadly, Python speech recognition and Speech-to-Text solutions can be categorized into two main types: open-source libraries and cloud-based services. wav2letter (now part of Flashlight) appeals to those intrigued by convolutional neural network-based architectures but comes with significant setup challenges.

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Python Convolutional Neural Networks Neural Network OpenAI 130

Marktechpost

OCTOBER 4, 2024

Evaluated Models Ready Tensor’s benchmarking study categorized the 25 evaluated models into three main types: Machine Learning (ML) models, Neural Network models, and a special category called the Distance Profile model. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

Neural Network 103

Neural Network Machine Learning Convolutional Neural Networks Categorization 103

Towards AI

APRIL 7, 2024

Supervised learning can be applied to GIS applications such as species habitat mapping, land cover categorization, and temperature and precipitation prediction. Deep learning multiple– layer artificial neural networks are the basis of deep learning, a subdivision of machine learning (hence the word “deep”).

Machine Learning 105

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 105

Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

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Neural Network Auto-classification Convolutional Neural Networks Auto-complete 87

Marktechpost

NOVEMBER 27, 2024

Classification: Categorizing data into discrete classes (e.g., It’s a simple yet effective algorithm, particularly well-suited for text classification problems like spam filtering, sentiment analysis, and document categorization. Document categorization. Regression: Predicting continuous numerical values (e.g.,

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Machine Learning Algorithm Neural Network Convolutional Neural Networks 113

Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. For clarity, Tensorflow and Pytorch can be used for building neural networks. It was envisioned by Thongsuwan et al., It was envisioned by Thongsuwan et al.,

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Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

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. These tasks require the model to categorize edge types or predict the existence of an edge between two given nodes.

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Neural Network Convolutional Neural Networks Auto-classification Deep Learning 59

Viso.ai

FEBRUARY 11, 2024

It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. 2015 – Microsoft researchers report that their Convolutional Neural Networks (CNNs) exceed human ability in pure ILSVRC tasks. One of the crucial tasks in today’s AI is the image classification.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Pickl AI

JULY 28, 2023

Machine Learning algorithms can extract pertinent information from photos and generate precise predictions about the content or objects present using methods like Convolutional Neural Networks (CNNs). Training the Model: The labeled dataset is used to train a Machine Learning model, typically a deep neural network like a CNN.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

OCTOBER 7, 2024

Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e., Waste Categorization : Based on the classification, the waste is sorted into predefined categories (e.g., plastic, metal, paper).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Robotics 52

Marktechpost

NOVEMBER 21, 2024

Deep learning, characterized by neural networks, has emerged as a particularly powerful approach that learns multiple data abstractions through backpropagation. These networks demonstrate remarkable flexibility in representing complex data patterns and adapting to diverse computational challenges.

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Viso.ai

NOVEMBER 5, 2024

They categorized these experiments as Bag of Freebies (BoF) and Bag of Specials (BoS). Convolution Layer: The concatenated feature descriptor is then passed through a Convolution Neural Network. The convolutional operation helps to learn spatial relationships and further refines the attention map.

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Convolutional Neural Networks Neural Network Categorization Automation 98

Chatbots Life

APRIL 16, 2024

It is frequently used in tasks involving categorization. Artificial Neural Networks (ANN): ANNs are flexible discriminative models composed of interconnected layers of artificial neurons. Convolutional Neural Networks (CNN): CNNs are specialized deep learning models commonly used for image classification tasks.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

Pickl AI

AUGUST 28, 2024

Unlike regression, which deals with continuous output variables, classification involves predicting categorical output variables. They are easy to interpret and can handle both categorical and numerical data. Understand the unique characteristics and challenges of each type to apply the right approach effectively.

Machine Learning 52

Machine Learning Algorithm Categorization Neural Network 52

Mlearning.ai

FEBRUARY 6, 2023

How to Train a CNN Using tf.GradientTape A simple practical example of how to use TensorFlow's GradientTape to train a convolutional neural network.

Neural Network 52

Neural Network Convolutional Neural Networks Categorization Deep Learning 52

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g.,

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Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 113

Viso.ai

OCTOBER 10, 2024

The identification of regularities in data can then be used to make predictions, categorize information, and improve decision-making processes. While explorative pattern recognition aims to identify data patterns in general, descriptive pattern recognition starts by categorizing the detected patterns.

Neural Network 98

Neural Network Deep Learning Computer Vision Machine Learning 98

Heartbeat

MARCH 8, 2023

Monitoring A Convolutional Neural Network (CNN) in Comet — by Oluseye Jeremiah A convolutional neural network (CNN) is primarily used for image classification. Convolutional, pooling, and fully linked layers are some of the layers that make up a CNN.

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Viso.ai

DECEMBER 6, 2024

Autonomous Driving applying Semantic Segmentation in autonomous vehicles Semantic segmentation is now more accurate and efficient thanks to deep learning techniques that utilize neural network models. Additionally, autoencoders acquire the ability to rebuild input images while preserving important details for semantic segmentation.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Pickl AI

DECEMBER 10, 2024

Categorical Features (Nominal vs. Ordinal) Categorical features group data into distinct categories or classes, often representing qualitative attributes. Handling categorical data appropriately is essential for ensuring accurate interpretations by Machine Learning models.

Machine Learning 52

Machine Learning Convolutional Neural Networks Categorization Automation 52

Viso.ai

DECEMBER 20, 2023

However, unsupervised learning has its own advantages, such as being more resistant to overfitting (the big challenge of Convolutional Neural Networks ) and better able to learn from complex big data, such as customer data or behavioral data without an inherent structure.

Computer Vision 98

Computer Vision Machine Learning Neural Network Algorithm 98

Viso.ai

NOVEMBER 4, 2024

Computer Vision Model for Solar Prediction The researchers based their solution on computer vision, specifically deep Convolutional neural networks (CNNs) for object localization and identification. That provides numerous benefits, although other computer vision-based techniques might be utilized as well.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

Viso.ai

DECEMBER 22, 2023

The Segment Anything Model Technical Backbone: Convolutional, Generative Networks, and More Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) play a foundational role in the capabilities of SAM.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Heartbeat

FEBRUARY 22, 2023

Deep learning models can extract different, and often more useful, features compared to traditional machine learning models for several reasons: Depth Deep learning models, especially Convolutional Neural Networks (CNNs), have multiple layers that can learn hierarchical representations of the input data.

Deep Learning 52

Deep Learning Machine Learning Convolutional Neural Networks Computer Vision 52

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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