Flipboard

NOVEMBER 14, 2024

Machine learning (ML) models contain numerous adjustable settings called hyperparameters that control how they learn from data. Unlike model parameters that are learned automatically during training, hyperparameters must be carefully configured by developers to optimize model performance.

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Marktechpost

OCTOBER 31, 2024

Don’t Forget to join our 55k+ ML SubReddit. Trending ] LLMWare Introduces Model Depot: An Extensive Collection of Small Language Models (SLMs) for Intel PCs The post XElemNet: A Machine Learning Framework that Applies a Suite of Explainable AI (XAI) for Deep Neural Networks in Materials Science appeared first on MarkTechPost.

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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. Dont Forget to join our 75k+ ML SubReddit. This approach uses ideas from how biological dendrites work.

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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

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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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. Unity makes strength.

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Marktechpost

APRIL 6, 2024

In deep learning, a unifying framework to design neural network architectures has been a challenge and a focal point of recent research. The researchers tackle the core issue of the absence of a general-purpose framework capable of addressing both the specification of constraints and their implementations within neural network models.

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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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Analytics Vidhya

FEBRUARY 24, 2023

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

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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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Unite.AI

JANUARY 18, 2025

While Central Processing Units (CPUs) and Graphics Processing Units (GPUs) have historically powered traditional computing tasks and graphics rendering, they were not originally designed to tackle the computational intensity of deep neural networks.

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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

SEPTEMBER 6, 2024

Spiking Neural Networks (SNNs), a family of artificial neural networks that mimic the spiking behavior of biological neurons, have been in discussion in recent times. These networks provide a fresh method for working with temporal data, identifying the complex relationships and patterns seen in sequences.

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Marktechpost

OCTOBER 18, 2024

Deep neural networks are powerful tools that excel in learning complex patterns, but understanding how they efficiently compress input data into meaningful representations remains a challenging research problem. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Marktechpost

MAY 28, 2024

Traditionally, Recurrent Neural Networks (RNNs) have been used for their ability to process sequential data efficiently despite their limitations in parallel processing. Rapid machine learning advancement has highlighted existing models’ limitations, particularly in resource-constrained environments.

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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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Marktechpost

AUGUST 4, 2024

Representational similarity measures are essential tools in machine learning, used to compare internal representations of neural networks. These measures help researchers understand learning dynamics, model behaviors, and performance by providing insights into how different neural network layers and architectures process information.

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Marktechpost

JANUARY 23, 2024

There are two major challenges in visual representation learning: the computational inefficiency of Vision Transformers (ViTs) and the limited capacity of Convolutional Neural Networks (CNNs) to capture global contextual information. Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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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

MARCH 9, 2024

The evolution of artificial intelligence, particularly in the realm of neural networks, has significantly advanced our data processing and analysis capabilities. Among these advancements, the efficiency of training and deploying deep neural networks has become a paramount focus.

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Marktechpost

MARCH 11, 2024

Advancements in machine learning, specifically in designing neural networks, have made significant strides thanks to Neural Architecture Search (NAS). Join our 38k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. Also, don’t forget to follow us on Twitter and Google News.

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Marktechpost

JUNE 30, 2024

Deep learning models like Convolutional Neural Networks (CNNs) and Vision Transformers achieved great success in many visual tasks, such as image classification, object detection, and semantic segmentation. Join our Telegram Channel and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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How to Learn Machine Learning

APRIL 17, 2025

In this article we will explore the Top AI and ML Trends to Watch in 2025: explain them, speak about their potential impact, and advice on how to skill up on them. Heres a look at the top AI and ML trends that are set to shape 2025, and how learners can stay prepared through programs like an AI ML course or an AI course in Hyderabad.

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ML Machine Learning Data Scientist Explainability 59

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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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

JANUARY 20, 2025

AI and ML are expanding at a remarkable rate, which is marked by the evolution of numerous specialized subdomains. introduced the concept of Generative Adversarial Networks (GANs) , where two neural networks, i.e., the generator and the discriminator, are trained simultaneously. Dont Forget to join our 65k+ ML SubReddit.

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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? The two main types of traditional ML algorithms are supervised and unsupervised. Data Preprocessing and Feature Engineering: Traditional ML requires extensive preprocessing to transform datasets as per model requirements.

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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. The first lesson many AI practitioners learn is that ML is more accessible than one might think. Its helpful to start by choosing a project that is both interesting and manageable within the scope of ML.

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Marktechpost

SEPTEMBER 10, 2024

The intersection of computational physics and machine learning has brought significant progress in understanding complex systems, particularly through neural networks. Traditional neural networks, including many adapted to consider Hamiltonian properties, often need help with these systems’ high dimensionality and complexity.

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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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Neural Network Machine Learning ML Artificial Intelligence 138

Marktechpost

FEBRUARY 9, 2025

Meta AI introduces Brain2Qwerty , a neural network designed to decode sentences from brain activity recorded using EEG or magnetoencephalography (MEG). Model Architecture and Its Potential Benefits Brain2Qwerty is a three-stage neural network designed to process brain signals and infer typed text.

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Unite.AI

APRIL 6, 2025

Wendys AI-Powered Drive-Thru System (FreshAI) FreshAI uses advanced natural language processing (NLP) , machine learning (ML) , and generative AI to optimize the fast-food ordering experience. FreshAI enhances order speed, accuracy, and personalization, setting a new benchmark for AI-driven automation in quick-service restaurants (QSRs).

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AI News

DECEMBER 15, 2023

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms. And that’s a big struggle,” explains Grande.

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Unite.AI

MARCH 15, 2024

Nevertheless, addressing the cost-effectiveness of ML models for business is something companies have to do now. For businesses beyond the realms of big tech, developing cost-efficient ML models is more than just a business process — it's a vital survival strategy. Challenging Nvidia, with its nearly $1.5

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Unite.AI

MARCH 8, 2024

Tiny AI excels in efficiency, adaptability, and impact by utilizing compact neural networks , streamlined algorithms, and edge computing capabilities. Inspired by its neural networks, these chips intricately weave silicon synapses, seamlessly connecting in a cerebral dance.

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Unite.AI

SEPTEMBER 21, 2023

Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models. AI and machine learning (ML) algorithms are capable of the following: Analyzing transaction patterns to detect fraudulent activities made by bots. What is Blockchain?

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Marktechpost

JANUARY 5, 2024

Also, don’t forget to join our 35k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , LinkedIn Gr oup , Twitter , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more. If you like our work, you will love our newsletter.

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