Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning?

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

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Unite.AI

FEBRUARY 7, 2024

Machine learning (ML) is a powerful technology that can solve complex problems and deliver customer value. However, ML models are challenging to develop and deploy. MLOps are practices that automate and simplify ML workflows and deployments. MLOps make ML models faster, safer, and more reliable in production.

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Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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Machine Learning Mastery

NOVEMBER 22, 2024

This well-known motto perfectly captures the essence of ensemble methods: one of the most powerful machine learning (ML) approaches -with permission from deep neural networks- to effectively address complex problems predicated on complex data, by combining multiple models for addressing one predictive task.

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Marktechpost

DECEMBER 28, 2024

Additionally, current approaches assume a one-to-one mapping between input samples and their corresponding optimized weights, overlooking the stochastic nature of neural network optimization. It uses a hypernetwork, which predicts the parameters of the task-specific network at any given optimization step based on an input condition.

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Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Programming Languages: Python (most widely used in AI/ML) R, Java, or C++ (optional but useful) 2.

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Marktechpost

NOVEMBER 16, 2024

Over two weeks, you’ll learn to extract features from images, apply deep learning techniques for tasks like classification, and work on a real-world project to detect facial key points using a convolutional neural network (CNN). Key topics include CNNs, RNNs, SLAM, and object tracking.

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Analytics Vidhya

APRIL 24, 2023

Introduction We live in a world where social media platforms shape our interests, tailor our news feeds, and provide customized content, all thanks to machine learning! With machine learning (ML), a branch of artificial intelligence (AI), software programs can predict outcomes more accurately without being explicitly instructed.

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Unite.AI

JANUARY 18, 2025

A Neural Processing Unit (NPU) is a specialized microprocessor built from the ground up to handle the unique requirements of modern AI and machine learning workloads. This parallelism is critical for deep learning tasks, where training and inference involve large batches of data.

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. One of the central challenges in this field is the extended time needed to train complex neural networks.

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AI News

DECEMBER 15, 2023

In an interview at AI & Big Data Expo , Alessandro Grande, Head of Product at Edge Impulse , discussed issues around developing machine learning models for resource-constrained edge devices and how to overcome them. “A lot of the companies building edge devices are not very familiar with machine learning,” says Grande.

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Marktechpost

MARCH 21, 2024

The fields of Machine Learning (ML) and Artificial Intelligence (AI) are significantly progressing, mainly due to the utilization of larger neural network models and the training of these models on increasingly massive datasets. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Machine Learning ML Neural Network Artificial Intelligence 128

Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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Marktechpost

OCTOBER 4, 2024

Ready Tensor conducted an extensive benchmarking study to evaluate the performance of 25 machine learning models on five distinct datasets to improve time series step classification accuracy in their latest publication on Time Step Classification Benchmarking. If you like our work, you will love our newsletter. Let’s collaborate!

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Marktechpost

FEBRUARY 2, 2025

Artificial Neural Networks (ANNs) have their roots established in the inspiration developed from biological neural networks. dANNs offer a new way to build artificial neural networks. Their learning is highly accurate, remarkably strong and exceptionally parameter-efficient.

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Marktechpost

APRIL 26, 2024

More sophisticated methods like TARNet, Dragonnet, and BCAUSS have emerged, leveraging the concept of representation learning with neural networks. In some cases, the neural network might detect and rely on interactions between variables that don’t actually have a causal relationship.

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Marktechpost

MARCH 23, 2024

Don’t Forget to join our 39k+ ML SubReddit The post FeatUp: A Machine Learning Algorithm that Upgrades the Resolution of Deep Neural Networks for Improved Performance in Computer Vision Tasks appeared first on MarkTechPost. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Marktechpost

JUNE 22, 2024

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. Key technical aspects include the use of various neural network architectures (MLPs, CNNs, ViTs) and optimizers (SGD, Adam, AdamW, Shampoo).

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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Marktechpost

FEBRUARY 4, 2024

Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving.

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Marktechpost

MAY 28, 2024

Sequence modeling is a critical domain in machine learning, encompassing applications such as reinforcement learning, time series forecasting, and event prediction. Rapid machine learning advancement has highlighted existing models’ limitations, particularly in resource-constrained environments.

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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. Don’t Forget to join our 55k+ ML SubReddit. If you like our work, you will love our newsletter.

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Machine Learning Neural Network Data Analysis Large Language Models 85

Marktechpost

JULY 23, 2024

The recent machine-learning (ML) models have remarkably succeeded in short-term weather forecasts. GoogleAI’s NeuralGCM is a hybrid model combining a differentiable solver for atmospheric dynamics with machine-learning components for parameterizing physical processes.

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Unite.AI

SEPTEMBER 4, 2024

Aarki allows brands to effectively engage audiences in a privacy-first world by using billions of contextual bidding signals coupled with proprietary machine learning and behavioral models. Can you elaborate on how Aarki's multi-level machine-learning infrastructure works?

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Marktechpost

JANUARY 20, 2025

AI and ML are expanding at a remarkable rate, which is marked by the evolution of numerous specialized subdomains. While they share foundational principles of machine learning, their objectives, methodologies, and outcomes differ significantly. Dont Forget to join our 65k+ ML SubReddit. Ian Goodfellow et al.

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Marktechpost

AUGUST 4, 2024

Representational similarity measures are essential tools in machine learning, used to compare internal representations of neural networks. The problem is compounded by the diversity of neural network architectures and their various tasks.

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Marktechpost

OCTOBER 7, 2024

Modern machine learning (ML) phenomena such as double descent and benign overfitting have challenged long-standing statistical intuitions, confusing many classically trained statisticians. Various researchers have attempted to unravel the complexities of modern ML phenomena.

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Marktechpost

APRIL 26, 2024

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. The rapid evolution and immense potential of Graph ML pose a need for conducting a comprehensive review of recent advancements in Graph ML.

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Analytics Vidhya

FEBRUARY 24, 2023

Introduction Are you interested in learning about Apache Spark and how it has transformed big data processing? Or maybe you’re curious about how to implement a neural network using PyTorch. Or perhaps you want to explore the exciting world of AI and its career opportunities?

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Unite.AI

FEBRUARY 6, 2025

AI and machine learning (ML) are reshaping industries and unlocking new opportunities at an incredible pace. It's common to initially think that learning to develop AI technologies requires an advanced degree or a background working in a research lab.

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Marktechpost

JANUARY 13, 2024

The challenge of interpreting the workings of complex neural networks, particularly as they grow in size and sophistication, has been a persistent hurdle in artificial intelligence. The traditional methods of explaining neural networks often involve extensive human oversight, limiting scalability.

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Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Transformer architectures, exemplified by models like ChatGPT, have revolutionized natural language processing tasks.

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AI Weekly

JULY 13, 2023

psychologytoday.com Decoding How Spotify Recommends Music to Users Machine learning (ML) and artificial intelligence (AI) have revolutionized the music streaming industry by enhancing the user experience, improving content discovery, and enabling personalized recommendations. [Try Pluto for free today] pluto.fi AlphaGO was.

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Marktechpost

SEPTEMBER 3, 2024

Recent neural architectures remain inspired by biological nervous systems but lack the complex connectivity found in the brain, such as local density and global sparsity. Researchers from Microsoft Research Asia introduced CircuitNet, a neural network inspired by neuronal circuit architectures.

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Marktechpost

FEBRUARY 21, 2024

Graph-based machine learning is undergoing a significant transformation, largely propelled by the introduction of Graph Neural Networks (GNNs). These networks have been pivotal in harnessing the complexity of graph-structured data, offering innovative solutions across various domains.

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Marktechpost

MAY 10, 2024

Redundant execution introduces the concept of a hybrid (convolutional) neural network designed to facilitate reliable neural network execution for safe and dependable AI. The method has scope for further extension to more complex neural network architectures and applications with additional optimization.

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