Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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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

APRIL 19, 2024

Google DeepMind has recently introduced Penzai, a new JAX library that has the potential to transform the way researchers construct, visualize, and alter neural networks. Penzai is a new approach to neural network development that emphasizes transparency and functionality. Installation pip install penzai Import Using.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Marktechpost

APRIL 29, 2024

Physics-Informed Neural Networks (PINNs) have become a cornerstone in integrating deep learning with physical laws to solve complex differential equations, marking a significant advance in scientific computing and applied mathematics.

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Flipboard

OCTOBER 25, 2023

Scientists have demonstrated that an AI system called a neural network can be trained to show "systematic compositionality," a key part of human …

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Marktechpost

MAY 13, 2024

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. Convolutional Neural Networks (CNNs) CNNs have been the cornerstone of image-processing tasks for years.

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Marktechpost

MAY 10, 2024

The inherent risks associated with AI systems, especially in applications like autonomous driving and medical diagnosis, where errors can have severe consequences, should be handled carefully, keeping the risk factor under control. Overall, the research significantly contributes to achieving safe and dependable AI systems.

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Marktechpost

MAY 25, 2024

Advancements in AI have led to proficient systems that make unclear decisions, raising concerns about deploying untrustworthy AI in daily life and the economy. Understanding neural networks is vital for trust, ethical concerns like algorithmic bias, and scientific applications requiring model validation.

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TensorFlow

FEBRUARY 6, 2024

Graph neural networks , or GNNs for short, have emerged as a powerful technique to leverage both the graph’s connectivity (as in the older algorithms DeepWalk and Node2Vec ) and the input features on the various nodes and edges. One classical approach is message-passing neural networks. Beyond supervised training (i.e.,

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Neural Network Software Engineer ML Python 123

Towards AI

JANUARY 9, 2024

Last Updated on January 10, 2024 by Editorial Team Author(s): Caspar Bannink Originally published on Towards AI. Welcome to part 3 of the AI basics series. In this post, we will take a look at the feedforward neural network. Join thousands of data leaders on the AI newsletter. Published via Towards AI

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Towards AI

FEBRUARY 1, 2024

Last Updated on February 1, 2024 by Editorial Team Author(s): Abhishek Chaudhary Originally published on Towards AI. Many of us have a functional understanding of neural networks and how they work. Neural networks are a type of machine model inspired by the structure of the human brain.

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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

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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Towards AI

APRIL 29, 2024

Last Updated on April 30, 2024 by Editorial Team Author(s): Sanket Rajaram Originally published on Towards AI. Understanding Convolutional Neural Network (CNN) — A Guide to Visual Recognition in the AI Era This article will help you understand the application of conventional artificial neural networks to visual recognition problems.

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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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Neural Network Natural Language Processing Categorization Explainability 123

Unite.AI

JULY 21, 2023

The regulation of AI in the arts is a hot topic. AI wants high quality music because the ultimate quality of output is heavily dependent on the quality of input. The aforementioned problem of input legitimacy is relevant for images and texts that are applicable for AI. Most, but not all. Not really.

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Flipboard

OCTOBER 8, 2023

Artificial intelligence startup Anthropic PBC says it has come up with a way to get a better understanding of the behavior of the neural networks that power its AI algorithms.The research could ha

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Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

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Marktechpost

MAY 14, 2024

While generative neural networks can efficiently simulate large-scale genomic data, challenges like computational complexity and model optimization persist. Sources: [link] [link] [link] The post Neural Networks and Nucleotides: AI in Genomic Manufacturing appeared first on MarkTechPost.

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Neural Network Deep Learning Artificial Intelligence Artificial Intelligence 82

Towards AI

DECEMBER 10, 2023

Last Updated on December 11, 2023 by Editorial Team Author(s): Adam Ross Nelson Originally published on Towards AI. Underlying math of neural networks by studying our predecessor’s ways One of the first to describe neural networks was neurophysiologis Warren McCulloch and mathematician Walter Pitts, as a model for biological brains.

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Marktechpost

MAY 17, 2024

Natural language processing (NLP) has advanced significantly thanks to neural networks, with transformer models setting the standard. Researchers from the Toyota Research Institute have introduced Scalable UPtraining for Recurrent Attention (SUPRA), a method to convert pre-trained transformers into recurrent neural networks (RNNs).

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Marktechpost

MAY 6, 2024

Convolutional Neural Networks (CNNs) have become the benchmark for computer vision tasks. Capsule Networks (CapsNets), first introduced by Hinton et al. Sources [link] [link] [link] The post Capsule Networks: Addressing Limitations of Convolutional Neural Networks CNNs appeared first on MarkTechPost.

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Marktechpost

APRIL 4, 2024

A deep Neural network is crucial in synthesizing photorealistic images and videos using large-scale image and video generative models. Also, the neural network weight, convolution, or linear layers remain the same for different conditions. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Neural Network AI AI ML 107

Marktechpost

MAY 5, 2024

Graph Neural Network (GNN)–based motion planning has emerged as a promising approach in robotic systems for its efficiency in pathfinding and navigation tasks. Path Quality: Paths are optimized based on the latent space encoded by the network. Let’s delve into the detailed specifics of the three prominent systems: 1.

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Neural Network Robotics Deep Learning Computer Vision 102

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

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

JANUARY 24, 2024

This shift is driven by neural networks that learn through self-supervision, bolstered by specialized hardware. The reach of these transformations extends beyond the confines of computer science, influencing diverse fields such as robotics, biology, and chemistry, showcasing the pervasive impact of AI across various disciplines.

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Marktechpost

MAY 16, 2024

Artificial neural networks (ANNs) show a remarkable pattern when trained on natural data irrespective of exact initialization, dataset, or training objective; models trained on the same data domain converge to similar learned patterns. If you like our work, you will love our newsletter.

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Neural Network Machine Learning ML Artificial Intelligence 108

Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications. Join our AI Channel on Whatsapp.

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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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Neural Network Machine Learning Algorithm AI Researcher 140

Marktechpost

DECEMBER 21, 2023

A team of researchers from the University of Massachusetts Lowell, Eleuther AI, and Amazon developed a method known as ReLoRA, which uses low-rank updates to train high-rank networks. ReLoRA accomplishes a high-rank update, delivering a performance akin to conventional neural network training. parameters.

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Marktechpost

MAY 11, 2024

Graph Neural Networks (GNNs) are crucial in processing data from domains such as e-commerce and social networks because they manage complex structures. Traditionally, GNNs operate on data that fits within a system’s main memory. Check out the Paper. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Neural Network ML AI AI 93

Marktechpost

MAY 2, 2024

This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. The aim is to automate reverse engineering processes by training AI to understand binaries and provide English descriptions.

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Neural Network Machine Learning Large Language Models Automation 99

Towards AI

MAY 10, 2024

Last Updated on May 14, 2024 by Editorial Team Author(s): Ayo Akinkugbe Originally published on Towards AI. For example — using np.argmax on the output matrix for a multi-class neural network prediction produces the index label of the maximum value in the output matrix. Most real-world problems aren’t linearly separable.

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Neural Network Python AI AI 81

Marktechpost

NOVEMBER 7, 2023

Neural networks have become indispensable tools in various fields, demonstrating exceptional capabilities in image recognition, natural language processing, and predictive analytics. The sum of these vectors is then passed to the next layer, creating a sparse and discrete bottleneck within the network.

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Marktechpost

JANUARY 24, 2024

However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues. The model works by executing forward–backward cycles using a physical forward model and has an iterative-trained neural network.

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