Marktechpost

JULY 28, 2024

In the past decade, the data-driven method utilizing deep neural networks has driven artificial intelligence success in various challenging applications across different fields. LAMBDA provides an essential medium that allows smooth interaction between domain knowledge and AI capabilities in data science.

Data Analysis 135

Data Analysis AI Modeling Data Science Artificial Intelligence 135

Marktechpost

APRIL 24, 2024

MIT CSAIL researchers introduced MAIA (Multimodal Automated Interpretability Agent) to address the challenge of understanding neural models, especially in computer vision, where interpreting the behavior of complex models is essential for improving accuracy and robustness and identifying biases.

Neural Network 125

Neural Network Artificial Intelligence Artificial Intelligence Automation 125

Marktechpost

SEPTEMBER 26, 2023

In the domain of reasoning under uncertainty, probabilistic graphical models (PGMs) have long been a prominent tool for data analysis. They introduce Neural Graphical Models (NGMs), a novel type of PGM that leverages deep neural networks to learn and efficiently represent probability functions over a domain.

Neural Network 135

Neural Network Categorization Data Scientist Data Analysis 135

Marktechpost

DECEMBER 28, 2023

Purdue University’s researchers have developed a novel approach, Graph-Based Topological Data Analysis (GTDA), to simplify interpreting complex predictive models like deep neural networks. GTDA utilizes topological data analysis to transform intricate prediction landscapes into simplified topological maps.

Deep Learning 123

Deep Learning Data Analysis Neural Network Algorithm 123

Towards AI

DECEMBER 18, 2024

I have been in the Data field for over 8 years, and Machine Learning is what got me interested then, so I am writing about this! They chase the hype Neural Networks, Transformers, Deep Learning, and, who can forget AI and fall flat. More about me here. But heres the truth: Most beginners get lost in the noise. The secret?

Machine Learning 105

Machine Learning Neural Network Deep Learning Explainability 105

Marktechpost

MAY 18, 2024

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. The study reviews existing works related to GNNs, including graph convolutional networks, pooling operations, and attention mechanisms.

Neural Network 109

Neural Network Categorization Deep Learning Data Analysis 109

Marktechpost

NOVEMBER 11, 2024

Python has become the go-to language for data analysis due to its elegant syntax, rich ecosystem, and abundance of powerful libraries. Data scientists and analysts leverage Python to perform tasks ranging from data wrangling to machine learning and data visualization.

Data Analysis 91

Data Analysis Python Natural Language Processing Computer Vision 91

Unite.AI

NOVEMBER 6, 2023

High-Dimensional and Unstructured Data : Traditional ML struggles with complex data types like images, audio, videos, and documents. Adaptability to Unseen Data: These models may not adapt well to real-world data that wasn’t part of their training data. Prominent transformer models include BERT , GPT-4 , and T5.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Unite.AI

OCTOBER 2, 2024

By exploring how TorchGeo addresses these complexities, readers will gain insight into its potential for working with geospatial data. The Growing Importance of Machine Learning for Geospatial Data Analysis Geospatial data combines location-specific information with time, creating a complex network of data points.

Machine Learning 278

Machine Learning Convolutional Neural Networks Neural Network Data Scientist 278

NYU Center for Data Science

DECEMBER 3, 2024

The research revealed that regardless of whether a neural network is trained to recognize images from popular computer vision datasets like ImageNet or CIFAR, it develops similar internal patterns for processing visual information. Particularly in being extremely good at exploratory data analysis.”

Neural Network 68

Neural Network Computer Scientist Computer Vision Explainability 68

AI Weekly

JULY 13, 2023

However, sharing biomedical data can put sensitive personal information at risk. plos.org Screening for Chagas disease from the electrocardiogram using a deep neural network Worldwide, it is estimated that over 6 million people are infected with Chagas disease (ChD). [Try Pluto for free today] pluto.fi

Neural Network 242

Neural Network Robotics ChatGPT Computer Vision 242

AI News

JULY 22, 2024

Machine learning models, such as regression analysis, neural networks, and decision trees, are employed to analyse historical data and predict future outcomes. Predictive analytics: The heart of AI betting At the core of AI’s impact on sports betting is predictive analytics.

Machine Learning 301

Machine Learning Algorithm Neural Network Natural Language Processing 301

Towards AI

DECEMBER 16, 2024

Kernel Arnold Networks (KAN) Summary: Kernel Arnold Networks (KAN) propose a new way of representing and processing data, challenging traditional deep neural networks. Application to a broad range of tasks, including physics-based simulations and temporal data analysis.

AI Research 111

AI Research AI Researcher Computer Vision Neural Network 111

Unite.AI

SEPTEMBER 21, 2023

As a result, AI improves productivity, reduces human error, and facilitates data-driven decision-making for all stakeholders. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models. What is Blockchain? Both technologies complement each other.

Convolutional Neural Networks 289

Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 289

Unite.AI

NOVEMBER 8, 2024

Together with Viacheslav Matiunin, Wayvee’s CTO and a physicist who led data analysis for the LHCb experiment at CERN, and a group of researchers and neuroscientists, we built a prototype using a regular Wi-Fi router to test the idea. These are key indicators for understanding emotional states.

Emotion AI 147

Emotion AI Algorithm Explainability Neural Network 147

Unite.AI

FEBRUARY 6, 2025

Such a project would introduce concepts that include data analysis, feature engineering, and model evaluation while also providing a deep understanding of the ML lifecyclea key framework for systematically solving problems. Its helpful to start by choosing a project that is both interesting and manageable within the scope of ML.

Artificial Intelligence 166

Artificial Intelligence Artificial Intelligence ML Responsible AI 166

Pickl AI

SEPTEMBER 3, 2024

Summary: Artificial Neural Network (ANNs) are computational models inspired by the human brain, enabling machines to learn from data. Introduction Artificial Neural Network (ANNs) have emerged as a cornerstone of Artificial Intelligence and Machine Learning , revolutionising how computers process information and learn from data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

Unite.AI

MAY 8, 2024

He began his career at Yandex in 2017, concurrently studying at the Yandex School of Data Analysis. This interest led me to the Yandex School of Data Analysis, a highly competitive machine learning master's degree program in Russia (only 200 people are accepted each year).

Machine Learning 298

Machine Learning Software Engineer Large Language Models Data Analysis 298

Marktechpost

NOVEMBER 26, 2024

Real-world networks, such as those in biomedical and multi-omics datasets, often present complex structures characterized by multiple types of nodes and edges, making them heterogeneous or multiplex. Already existing approaches have attempted to handle such complexities in heterogeneous and multiplex networks by different forms of strategies.

Machine Learning 87

Machine Learning Neural Network Data Analysis Large Language Models 87

Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

IBM Journey to AI blog

DECEMBER 20, 2023

Many retailers’ e-commerce platforms—including those of IBM, Amazon, Google, Meta and Netflix—rely on artificial neural networks (ANNs) to deliver personalized recommendations. They’re also part of a family of generative learning algorithms that model the input distribution of a given class or/category.

Machine Learning 230

Machine Learning Neural Network Algorithm Computer Vision 230

Marktechpost

DECEMBER 15, 2023

CNNs (Convolutional neural networks) have become a popular technique for image recognition in recent years. However, new challenges have emerged as these networks have grown more complex. They have been highly successful in object detection, classification, and segmentation tasks.

Data Analysis 113

Data Analysis Convolutional Neural Networks Neural Network AI 113

Unite.AI

SEPTEMBER 18, 2023

The above image depicts the architecture of deep learning perceptron, and as it can be seen in the image, a deep learning framework employs a multiple-level neural network architecture to learn the features in the data.

Deep Learning 321

Deep Learning Artificial Intelligence Artificial Intelligence AI 321

Pickl AI

NOVEMBER 22, 2024

Summary: Long Short-Term Memory (LSTM) networks are a specialised form of Recurrent Neural Networks (RNNs) that excel in learning long-term dependencies in sequential data. Understanding Recurrent Neural Networks (RNNs) To appreciate LSTMs, it’s essential to understand RNNs.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing NLP 52

Pickl AI

JANUARY 26, 2025

Introduction Deep Learning models transform how we approach complex problems, offering powerful tools to analyse and interpret vast amounts of data. These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

AI News

OCTOBER 15, 2024

Unsupervised machine learning systems use artificial neural networks to continue interacting with customers and retain existing customers. Personalised recommendations: Using data analysis and machine learning can tailor personalised suggestions to customers based on past interactions, behaviours, and preferences.

Machine Learning 290

Machine Learning Chatbots Business Intelligence Automation 290

Marktechpost

MAY 5, 2024

The course covers numerous algorithms of supervised and unsupervised learning and also teaches how to build neural networks using TensorFlow. It covers topics like image processing, cluster analysis, gradient boosting, and popular libraries like scikit-learn, Spark, and Keras.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

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

Marktechpost

OCTOBER 23, 2024

From the statistical foundations of machine learning to the complex algorithms powering neural networks, mathematics plays a pivotal role in shaping the capabilities and limitations of AI. Derivatives are key to optimizing functions like the loss function in neural networks by measuring rates of change.

Neural Network 116

Neural Network Algorithm Robotics Machine Learning 116

Unite.AI

JULY 24, 2024

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. The company ensures that all AI processes are conducted on-device, meaning that user data never leaves the device unless explicitly allowed by the user.

Machine Learning 223

Machine Learning NLP AI AI 223

Mlearning.ai

MAY 8, 2023

A comprehensive step-by-step guide with data analysis, deep learning, and regularization techniques Introduction In this article, we will use different deep-learning TensorFlow neural networks to evaluate their performances in detecting whether cell nuclei mass from breast imaging is malignant or benign. perimeter d.

Neural Network 52

Neural Network Deep Learning Machine Learning Data Analysis 52

Marktechpost

MAY 1, 2024

While human intelligence relies on biological neural networks and operates at slower speeds, AI systems leverage digital processors for rapid data processing and seamless communication. Unlike humans, AI systems can easily update and scale, but they consume more energy.

Neural Network 113

Neural Network Artificial Intelligence Artificial Intelligence Explainability 113

Marktechpost

JANUARY 15, 2024

One recent study claims to have found a more accurate model for predicting human decisions using neural networks trained on a large online dataset called choices13k. To test this idea, they created a new model that added structured decision noise to a neural network trained on data from a traditional laboratory study.

Machine Learning 100

Machine Learning Neural Network Data Analysis Explainability 100

Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. OmniXAI aims to simplify XAI for data scientists and practitioners across different ML process stages. Image Source 10.

Explainable AI 117

Explainable AI Explainability Neural Network Data Scientist 117

Pickl AI

AUGUST 27, 2024

Summary: Python simplicity, extensive libraries like Pandas and Scikit-learn, and strong community support make it a powerhouse in Data Analysis. It excels in data cleaning, visualisation, statistical analysis, and Machine Learning, making it a must-know tool for Data Analysts and scientists. Why Python?

Data Analysis 52

Data Analysis Python Machine Learning Data Mining 52

Marktechpost

MARCH 31, 2024

Whether you’re a beginner, a seasoned data scientist, or someone interested in leveraging data in your work, our carefully selected list of top data science books for 2024 offers a comprehensive guide. The book covers topics like supervised learning, unsupervised learning, neural networks, support vector machines, etc.

Data Science 120

Data Science Data Mining Machine Learning Neural Network 120

Marktechpost

JANUARY 30, 2025

The consistent theme in these use cases is an AI-driven entity that moves beyond passive data analysis to dynamically and continuously sense, think, and act. Yet, before a system can take meaningful action, it must capture and interpret the data from which it forms its understanding.

Robotics 116

Robotics Convolutional Neural Networks Large Language Models AI 116