Marktechpost

JANUARY 2, 2024

Exploring pre-trained models for research often poses a challenge in Machine Learning (ML) and Deep Learning (DL). One solution to simplify the visualization of ML/DL models is the open-source tool called Netron. This process can be time-consuming and intricate, deterring quick access to model architectures.

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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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Marktechpost

JANUARY 24, 2024

Artificial Intelligence has witnessed a revolution, largely due to advancements in deep learning. This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. Before the advent of deep learning, data representation often involved manually curated feature vectors.

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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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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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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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The MLOps Blog

MARCH 13, 2025

In our paper Bayesian Deep Learning is Needed in the Age of Large-Scale AI , we argue that the case above is not the exception but rather the rule and a direct consequence of the research community’s focus on predictive accuracy as a single metric of interest. we might not know how fast the parade moves).

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Marktechpost

JULY 26, 2024

Deep learning has demonstrated remarkable success across various scientific fields, showing its potential in numerous applications. Sparsity in neural networks is one of the critical areas being investigated, as it offers a way to enhance the efficiency and manageability of these models.

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Marktechpost

MARCH 10, 2025

Deep neural networks’ seemingly anomalous generalization behaviors, benign overfitting, double descent, and successful overparametrization are neither unique to neural networks nor inherently mysterious. However, deep learning remains distinctive in specific aspects. Check out the Paper.

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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)? 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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Unite.AI

AUGUST 28, 2024

Deep Instinct is a cybersecurity company that applies deep learning to cybersecurity. As I learned about the possibilities of predictive prevention technology, I quickly realized that Deep Instinct was the real deal and doing something unique. ML is unfit for the task. He holds a B.Sc Not all AI is equal.

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Marktechpost

MAY 2, 2024

Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Thus, while MLPs remain crucial, there’s ongoing exploration for more effective nonlinear regressors in neural network design.

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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. On the other hand, SSMs are a promising approach for modeling sequential data in deep learning.

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Marktechpost

MARCH 26, 2024

Deep learning models have recently gained significant popularity in the Artificial Intelligence community. In order to address these challenges, a team of researchers has introduced DomainLab, a modular Python package for domain generalization in deep learning. If you like our work, you will love our newsletter.

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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

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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TensorFlow

FEBRUARY 6, 2024

Discrete mathematics and computer science have a long history of formalizing such networks them as graphs , consisting of nodes arbitrarily connected by edges in various irregular ways. Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases.

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Marktechpost

JANUARY 9, 2024

Deep learning methods have been widely employed for early disease detection to tackle this challenge, showcasing remarkable classification accuracy and data synthesis to bolster model training. The study acknowledges the limited research effort in investigating multimodal images related to breast cancer using deep learning techniques.

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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. Rather than learning to generate new data, these models aim to make accurate predictions.

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Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. I’ve passed many ML courses before, so that I can compare. So you definitely can trust his expertise in Machine Learning and Deep Learning.

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Marktechpost

NOVEMBER 25, 2023

Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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Neural Network Python Deep Learning Artificial Intelligence 125

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

JUNE 16, 2024

Topological Deep Learning (TDL) advances beyond traditional GNNs by modeling complex multi-way relationships, unlike GNNs that only capture pairwise interactions. This capability is critical for understanding complex systems like social networks and protein interactions. PyG and DGL cater to both GDL and general graph learning.

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Marktechpost

MARCH 23, 2024

AI researchers are taking the game to a new level with geometric deep learning. At its core, TacticAI relies on a cutting-edge geometric deep learning pipeline to turn raw football data into structured inputs for AI models to understand. Check out the Paper and Blog. Also, don’t forget to follow us on Twitter.

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Marktechpost

JANUARY 24, 2024

With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues.

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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 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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Deep Learning Machine Learning Python Computer Vision 120

Marktechpost

OCTOBER 13, 2023

In the realm of deep learning, the challenge of developing efficient deep neural network (DNN) models that combine high performance with minimal latency across a variety of devices remains. Check out the Paper and Reference Article. All Credit For This Research Goes To the Researchers on This Project.

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Marktechpost

JUNE 9, 2024

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Researchers at the Institute of Biomedical Engineering, TU Dresden, developed a deep learning architecture, xECGArch, for interpretable ECG analysis.

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Marktechpost

OCTOBER 8, 2023

Can we adapt these hierarchy organization and parallel processing techniques in deep learning? Yes, the field of study is called Neural networks. This provides an advantage to NDP as it can operate upon any neural network of arbitrary size or architecture. Check out the Paper.

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Marktechpost

NOVEMBER 8, 2023

The recent developments in the fields of Artificial Intelligence and Machine Learning have made everyone’s lives easier. With their incredible capabilities, AI and ML are diving into every industry and solving problems. Overconfidence is a prevalent issue, particularly in the context of deep neural networks.

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Marktechpost

SEPTEMBER 3, 2023

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. This layer adapts its kernel using a conditioning neural network, significantly enhancing Orchid’s ability to filter long sequences effectively.

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Bugra Akyildiz

FEBRUARY 26, 2025

Stanford CS224n: Natural Language Processing with Deep Learning Stanford’s CS224n stands as the gold standard for NLP education, offering a rigorous exploration of neural architectures, sequence modeling, and transformer-based systems. S191: Introduction to Deep Learning MIT’s 6.S191

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Marktechpost

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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Marktechpost

JANUARY 4, 2024

Qu Kun from the University of Science and Technology of the Chinese Academy of Sciences has created a solution called Spatial Architecture Characterization by Deep Learning (SPACEL). The post Meet SPACEL: A New Deep-Learning-based Analysis Toolkit for Spatial Transcriptomics appeared first on MarkTechPost.

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Marktechpost

DECEMBER 30, 2023

The remarkable potentials of Artificial Intelligence (AI) and Deep Learning have paved the way for a variety of fields ranging from computer vision and language modeling to healthcare, biology, and whatnot. SciML consists of three primary subfields, which include PDE solvers, PDE discovery, and operator learning.

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