Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Marktechpost

MARCH 4, 2024

The proposed method, ViSNet, introduces a vector-scalar interactive graph neural network framework designed to enhance molecular geometry modeling. Evaluation across various datasets demonstrates ViSNet’s superior performance compared to existing algorithms in predicting molecular properties and simulating molecular dynamics.

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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. However, this approach tends to overlook optimizing the search space itself. Check out the Paper and Reference Article. We are also on WhatsApp.

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Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

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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)? Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised.

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Marktechpost

JANUARY 19, 2024

Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood. To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. Check out the Paper.

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Marktechpost

JANUARY 25, 2024

It relies on the distortion of objects in a density-varying medium due to light refraction, with digital image correlation or optical flow algorithms used for analysis. Existing algorithms, mostly based on cross-correlation, are optimized for PIV and provide sparse velocity vectors. Michigan Ave.,

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Marktechpost

OCTOBER 8, 2023

Yes, the field of study is called Neural networks. Researchers at the University of Copenhagen present a graph neural network type of encoding in which the growth of a policy network is controlled by another network running in each neuron. They call it a Neural Developmental Program (NDP).

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Marktechpost

APRIL 6, 2024

One recent application consists of developing heuristic greedy algorithms for NP-hard problems and creating reward functions for robotics use. Also, researchers use the power of LLMs to develop innovative networking algorithms. Still, it is challenging for LLMs to rank these algorithms and pick the best one.

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Marktechpost

FEBRUARY 12, 2024

Deep convolutional neural networks (DCNNs) have been a game-changer for several computer vision tasks. As a result, many people are interested in finding ways to maximize the energy efficiency of DNNs through algorithm and hardware optimization. There are three notable characteristics of PDC in general.

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Marktechpost

JULY 15, 2023

Since the emergence of large-scale OT and Wasserstein GANs, machine learning has increasingly embraced using neural networks to solve optimum transport (OT) issues. The theoretical underpinnings of the algorithm make its output more easily understood than competing methods.

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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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Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. One of the most promising avenues for this kind of research is to learn from evolution and biological systems to improve the design of AI Models (after all Neural Networks were based on our brains).

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Towards AI

JANUARY 9, 2024

Now, I know, I know, we all want to work straight away on the latest and greatest technologies, which all work with Neural Networks. You see, AI is just the most general term for decision-making algorithms and does not necessarily mean ML. In other words, we all want to get directly into Deep Learning.

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Marktechpost

DECEMBER 16, 2023

Trained on a dataset from six UK hospitals, the system utilizes neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest X-ray findings from images and their free-text reports. An NLP algorithm, X-Raydar-NLP, was trained on 23,230 manually annotated reports to extract labels.

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Marktechpost

JANUARY 8, 2024

The framework utilizes a dynamic programming-based graph partition algorithm and a Simulated-Annealing-based (SA-based) approach for optimization. Gemini’s mapping component uses the SA algorithm with five operators tailored to efficiently explore the LP spatial mapping space. Also, don’t forget to follow us on Twitter.

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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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Mlearning.ai

AUGUST 22, 2023

Activation Functions in Neural Networks From the traditional Sigmoid and ReLU to cutting-edge activation functions like GeLU, this article delves into their significance, math, and guidelines for choosing the ideal function for your model. A Neural Network depicting Activations Functions in Action. Image by the Author.

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them.

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Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Below, we highlight a panoply of works that demonstrate Google Research’s efforts in developing new algorithms to address the above challenges.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work? The initial weights and biases of the neural network are randomly assigned.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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Marktechpost

DECEMBER 27, 2023

Its key advantage is the ability to train and deploy ML models directly within the database using standard SQL queries. It also allows managing open-source ML models from platforms like HuggingFace, providing a convenient way to track experiment results.

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. ’ The authors concluded that their extensive investigation into deep learning (DL) and machine learning (ML) algorithms in the context of biometric authentication yielded crucial insights. Check out the Paper.

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Marktechpost

MARCH 27, 2024

A team of researchers has now developed HETAL , an efficient HE-based algorithm (shown in Figure 1) for privacy-preserving transfer learning. At the core of HETAL is an optimized process for encrypted matrix multiplications, a dominant operation in neural network training. Also, don’t forget to follow us on Twitter.

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Unite.AI

MARCH 28, 2024

TacticAI utilizes geometric deep learning and graph neural networks (GNNs) as its foundational AI components. Graph Neural Networks (GNNs) are neural networks designed to process graph-structured data.

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Google Research AI blog

MARCH 30, 2023

Posted by Peter Mattson, Senior Staff Engineer, ML Performance, and Praveen Paritosh, Senior Research Scientist, Google Research, Brain Team Machine learning (ML) offers tremendous potential, from diagnosing cancer to engineering safe self-driving cars to amplifying human productivity. Each step can introduce issues and biases.

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Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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Unite.AI

AUGUST 29, 2023

In the past few years, Artificial Intelligence (AI) and Machine Learning (ML) have witnessed a meteoric rise in popularity and applications, not only in the industry but also in academia. It’s the major reason why its difficult to build a standard ML architecture for IoT networks.

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Marktechpost

SEPTEMBER 8, 2023

Researchers suggest a new approach to design using heuristic optimization and artificial neural networks to simplify the optimization process drastically. A deep neural network model replaced the 3D electromagnetic simulation of a Si-based MZM. If you like our work, you will love our newsletter.

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. Data Science enhances ML accuracy through preprocessing and feature engineering expertise.

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Marktechpost

APRIL 21, 2024

ML’s ability to approximate complex functions has led to algorithms that can solve, simulate, and even discover PDEs from data. TENG, including variants like TENG-Euler and TENG-Heun, achieves remarkable accuracy and efficiency in neural-network-based PDE solutions. Also, don’t forget to follow us on Twitter.

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Mlearning.ai

JULY 17, 2023

In today’s data-driven world, machine learning (ML) has become an indispensable tool for extracting valuable insights and making data-driven decisions. As a data scientist, staying ahead of the curve and continuously improving your skills is essential to tackle complex challenges in the field of ML. Happy learning!

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Towards AI

JULY 22, 2023

This is similar to how machine learning (ML) can seem at first. The algorithm learns from this data, understanding the relationship between the input and the output. So, Supervised Learning is like learning with a teacher who provides guidance and feedback, helping the algorithm to learn and make accurate predictions.

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Mlearning.ai

JULY 15, 2023

You have certainly seen it on Twitter, everyone is talking about this simple algorithm that beats powerful neural networks in… Continue reading on MLearning.ai »

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ML Review

MARCH 31, 2019

Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% This is, clearly, an optical illusion — but for the neural network.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). It involves feeding data to algorithms, which then generalize patterns and make inferences about unseen data. Model Selection Choosing the right algorithm for the task at hand is critical. predicting house prices).

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