NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Explainability 52

Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. More recent depth estimation frameworks deploy convolutional neural network structures to extract depth in a monocular image.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network Explainability Categorization 130

Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

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. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Viso.ai

FEBRUARY 8, 2024

Restricted Boltzmann Machines A Restricted Boltzmann Machine is a two-layer probabilistic neural network. Benefits of DBNs Deep belief networks use probabilistic modeling and a supervised learning approach to offer certain benefits over conventional neural networks. Faster training and better results.

Explainability 52

Explainability Neural Network Computer Vision Deep Learning 52

Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. According to researchers, this is the reason for developing GAN, an AI-based generative neural network trained using high-resolution radar precipitation fields.

Deep Learning 104

Deep Learning Convolutional Neural Networks Neural Network Artificial Intelligence 104

Mlearning.ai

MAY 11, 2023

Generative Pre-trained Transformer (GPT) Photo by Levart_Photographer on Unsplash Developed by OpenAI, GPT, which stands for “Generative Pre-trained Transformer,” is a neural network that has the ability to generate human-like language, making it an impressive tool for natural language processing (NLP).

Neural Network 52

Neural Network Explainability Convolutional Neural Networks Natural Language Processing 52

Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. In a typical neural network, you flatten your input one vector, take those input values in at once, multiply them by the weights in the first layer, add the bias, and pass the result into a neuron.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Towards AI

MAY 15, 2024

Source: Author/Adobe Firefly) Last week, as I scrolled through my Instagram feed, an animation video popped up in the famous 3Blue1Brown style explaining how Convolution Neural Networks work. First, generate a 2-minute transcript explaining the transformers architecture at a high-level. to explain what concepts.

Convolutional Neural Networks 78

Convolutional Neural Networks Explainability Python AI 78

Heartbeat

DECEMBER 14, 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. > Finished chain. . >

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

Heartbeat

APRIL 4, 2023

This comprehensive article will explain the fundamentals of neural style transfer (NST), provide an overview of the techniques used for performing NST, and discuss some best use cases for the application. It creates an intermediate representation of both images using a convolutional neural network (CNN).

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 52

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

Chatbots Life

OCTOBER 9, 2023

Researchers are using microwave imaging and convolutional neural networks for breast cancer screening with high accuracy in classifying profiles as healthy or diseased. ? Ethical considerations like bias and explainability must be addressed when using generative AI models.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Generative AI Deep Learning 52

Heartbeat

MAY 30, 2023

What are Generative Adversarial Networks (GANs)? GANs are a type of neural network design used to develop new data samples that are similar to the training data. This machine learning subset uses artificially generated neural networks to model complex data relationships. Assume you’re teaching your pet new tricks.

Neural Network 72

Neural Network Convolutional Neural Networks Deep Learning Machine Learning 72

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. TensorFlow is well-known for its versatility in designing custom layers.

Neural Network 52

Neural Network Computer Vision Robotics Convolutional Neural Networks 52

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.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 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).

Convolutional Neural Networks 99

Convolutional Neural Networks Algorithm Neural Network Machine Learning 99

Flipboard

JUNE 13, 2023

LOVO makes explainer videos, podcasts, social media content, and e-learning materials easy. Deep convolutional neural network-based image super-resolution is used. This AI-powered technology generates lifelike voices in a tenth of the time and cost of voice talent. It has an easy-to-use, full-featured UI.

AI Tools 80

AI Tools Convolutional Neural Networks Neural Network ChatGPT 80

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.

Neural Network 107

Neural Network Convolutional Neural Networks Algorithm Software Engineer 107

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.

Deep Learning 59

Deep Learning Convolutional Neural Networks Explainability Neural Network 59

Mlearning.ai

MARCH 30, 2023

To train a machine learning model or a neural network that can yield the best results requires what? How can we train a neural network without having an ample amount of data, even if you have it can you afford to train a model for months? Let me explain this in simple words. This is how transfer learning works.

Neural Network 52

Neural Network AI Developer AI Development Convolutional Neural Networks 52

Viso.ai

FEBRUARY 21, 2024

Graph Neural Networks (GNNs) are a type of neural network designed to directly operate on graphs, a data structure consisting of nodes (vertices) and edges connecting them. In this article, we’ll start with a gentle introduction to Graph Neural Networks and follow with a comprehensive technical deep dive.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 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. Recurrent neural networks (RNNs), are a significant deep learning approach used in time series analysis .

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Python 52

Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

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

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. 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.

Neural Network 59

Neural Network Convolutional Neural Networks Auto-classification Deep Learning 59

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.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Python 52

Heartbeat

JANUARY 15, 2024

Deep Learning Takes the Stage: A Symphony of Image Segmentation with Neural Networks In the previous sections, we explored the captivating world of active contour models (snakes) and accurate image segmentation. The ability to perform on-the-fly segmentation showcases the elegance and efficiency of active contour models.

Deep Learning 52

Deep Learning Computer Vision Neural Network Convolutional Neural Networks 52

Marktechpost

DECEMBER 19, 2023

In tackling the intricate task of predicting brain age, researchers introduce a groundbreaking hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) architectures. Importantly, the model’s performance includes R-square results, indicating a robust fit to the data.

Convolutional Neural Networks 131

Convolutional Neural Networks Neural Network Algorithm AI Researcher 131

Viso.ai

MAY 10, 2024

For neural networks, gradient descent is accomplished through a technique called backpropagation. Basic types of machine learning models Some common types of machine learning algorithms include: Regression models (e.g. text vs images) and (2) the desired output (e.g. a binary classification or a bounding box for an image).

Machine Learning 52

Machine Learning Computer Vision Neural Network Convolutional Neural Networks 52

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.

Computer Vision 52

Computer Vision Convolutional Neural Networks Emotion AI Deep Learning 52

PyImageSearch

FEBRUARY 6, 2023

This is the 3rd lesson in our 4-part series on OAK 101 : Introduction to OpenCV AI Kit (OAK) OAK-D: Understanding and Running Neural Network Inference with DepthAI API Training a Custom Image Classification Network for OAK-D (today’s tutorial) OAK 101: Part 4 To learn how to train an image classification network for OAK-D, just keep reading.

Auto-classification 144

Auto-classification Computer Vision Neural Network Deep Learning 144

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.

Machine Learning 52

Machine Learning Data Scientist Convolutional Neural Networks Algorithm 52