Marktechpost

OCTOBER 31, 2024

Deep learning has made advances in various fields, and it has made its way into material sciences as well. From tasks like predicting material properties to optimizing compositions, deep learning has accelerated material design and facilitated exploration in expansive materials spaces. Check out the Paper.

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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. Machine learning is a subset of AI. Your AI must be explainable, fair and transparent.

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AI News

JULY 24, 2023

AI News spoke with Damian Bogunowicz, a machine learning engineer at Neural Magic , to shed light on the company’s innovative approach to deep learning model optimisation and inference on CPUs. One of the key challenges in developing and deploying deep learning models lies in their size and computational requirements.

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Unite.AI

APRIL 15, 2025

Kirill Solodskih , PhD, is the Co-Founder and CEO of TheStage AI, as well as a seasoned AI researcher and entrepreneur with over a decade of experience in optimizing neural networks for real-world business applications. million in funding to fully automate neural network acceleration across any hardware platform.

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Marktechpost

MAY 14, 2024

Deep learning is crucial in today’s age as it powers advancements in artificial intelligence, enabling applications like image and speech recognition, language translation, and autonomous vehicles. Additionally, it offers insights into the diverse range of deep learning techniques applied across various industrial sectors.

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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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Towards AI

DECEMBER 18, 2024

I have been in the Data field for over 8 years, and Machine Learning is what got me interested then, so I am writing about this! They chase the hype Neural Networks, Transformers, Deep Learning, and, who can forget AI and fall flat. Youll learn faster than any tutorial can teach you. More about me here.

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Unite.AI

OCTOBER 10, 2024

A new research paper from Canada has proposed a framework that deliberately introduces JPEG compression into the training scheme of a neural network, and manages to obtain better results – and better resistance to adversarial attacks. In contrast, JPEG-DL (right) succeeds in distinguishing and delineating the subject of the photo.

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Analytics Vidhya

SEPTEMBER 27, 2022

Introduction My last blog discussed the “Training of a convolutional neural network from scratch using the custom dataset.” ” In that blog, I have explained: how to create a dataset directory, train, test and validation dataset splitting, and training from scratch. This blog is […].

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Unite.AI

NOVEMBER 28, 2024

1980s – The Rise of Machine Learning The 1980s introduced significant advances in machine learning , enabling AI systems to learn and make decisions from data. The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors.

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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. He holds a B.Sc Not all AI is equal.

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Becoming Human

MARCH 16, 2023

Photo by Paulius Andriekus on Unsplash Welcome back to the next part of this Blog Series on Graph Neural Networks! The following section will provide a little introduction to PyTorch Geometric , and then we’ll use this library to construct our very own Graph Neural Network!

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Unite.AI

OCTOBER 5, 2023

This is your third AI book, the first two being: “Practical Deep Learning: A Python-Base Introduction,” and “Math for Deep Learning: What You Need to Know to Understand Neural Networks” What was your initial intention when you set out to write this book? Different target audience.

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Unite.AI

JUNE 25, 2023

However, assimilating the understanding of physics into the realm of neural networks has proved challenging. In a significant breakthrough, the UCLA study intends to combine the deep understanding from data and the real-world know-how of physics, thereby creating a hybrid AI with augmented capabilities.

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Unite.AI

FEBRUARY 25, 2025

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. For industries reliant on neural networks, ensuring robustness and safety is critical.

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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.

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Towards AI

JANUARY 27, 2023

Deep Learning Explained: Perceptron The key concept behind every neural network. Source: Image by Gerd Altmann from Pixabay Nowadays, frameworks such as Keras, TensorFlow, or PyTorch provide turnkey access to most deep learning solutions without necessarily having to understand them in depth.

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Unite.AI

OCTOBER 24, 2024

Modern AI models, particularly those based on deep learning and neural networks , are incredibly power-hungry. Unlike traditional neural networks, which rely on complex mathematical computations and massive datasets, Tsetlin Machines employ a more straightforward, rule-based approach.

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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. Despite their widespread usage, the theoretical foundations of transformers have yet to be fully explored.

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Marktechpost

MAY 8, 2024

Over the past decade, advancements in deep learning and artificial intelligence have driven significant strides in self-driving vehicle technology. Deep learning and AI technologies play crucial roles in both modular and End2End systems for autonomous driving. Classical methodologies for these tasks are also explored.

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PyImageSearch

OCTOBER 28, 2024

Home Table of Contents NeRFs Explained: Goodbye Photogrammetry? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Steps Next Steps Citation Information NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work?

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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

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

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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Deep Learning Python Neural Network Artificial Intelligence 126

NVIDIA

JANUARY 6, 2025

NVIDIA GPUs and platforms are at the heart of this transformation, Huang explained, enabling breakthroughs across industries, including gaming, robotics and autonomous vehicles (AVs). The latest generation of DLSS can generate three additional frames for every frame we calculate, Huang explained.

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Towards AI

NOVEMBER 19, 2023

Photo by Pietro Jeng on Unsplash Deep learning is a type of machine learning that utilizes layered neural networks to help computers learn from large amounts of data in an automated way, much like humans do. We will explain intuitively what each one means and how it contributes to the deep learning process.

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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. So you definitely can trust his expertise in Machine Learning and Deep Learning. Neural Network is a combination of linear functions and activations.

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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. Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features.

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Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. The truth is, I couldn’t find anything.

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Marktechpost

JULY 7, 2024

Deep neural networks (DNNs) come in various sizes and structures. The specific architecture selected along with the dataset and learning algorithm used, is known to influence the neural patterns learned. Currently, a major challenge faced in the theory of deep learning is the issue of scalability.

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Marktechpost

NOVEMBER 29, 2023

While classical cognitive models explain many psychological features of speech perception, these models fall short in explaining brain coding and natural speech recognition. Deep learning models are getting close to human performance in automated speech recognition.

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Marktechpost

APRIL 24, 2024

The increasing complexity of AI systems, particularly with the rise of opaque models like Deep Neural Networks (DNNs), has highlighted the need for transparency in decision-making processes. ELI5 is a Python package that helps debug machine learning classifiers and explain their predictions.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Marktechpost

JANUARY 19, 2024

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

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Neural Network Deep Learning Explainable AI Machine Learning 111

PyImageSearch

DECEMBER 9, 2024

In this tutorial, you will learn about 3D Gaussian Splatting. This lesson is the last of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion NeRFs Explained: Goodbye Photogrammetry? this tutorial) To learn more about 3D Gaussian Splatting, just keep reading.

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IBM Journey to AI blog

AUGUST 9, 2024

Most generative AI models start with a foundation model , a type of deep learning model that “learns” to generate statistically probable outputs when prompted. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics.

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