Marktechpost

DECEMBER 18, 2023

In a groundbreaking study, MIT researchers have delved into the realm of deep neural networks, aiming to unravel the mysteries of the human auditory system. The foundation of this research builds upon prior work where neural networks were trained to perform specific auditory tasks, such as recognizing words from audio signals.

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Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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PyImageSearch

OCTOBER 14, 2024

This blog post is the 1st of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion (this blog post) 3D Reconstruction: Have NeRFs Removed the Need for Photogrammetry? The second blog post will introduce you to NeRFs , the neural network solution.

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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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Heartbeat

JUNE 14, 2023

Introduction Deep neural network classifiers have been shown to be mis-calibrated [1], i.e., their prediction probabilities are not reliable confidence estimates. For example, if a neural network classifies an image as a “dog” with probability p , p cannot be interpreted as the confidence of the network’s predicted class for the image.

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Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? In the same way, artificial neural networks (ANNs) were developed inspired by neurons in the brain. ANN approach is a machine learning algorithm inspired by biological neural networks. Neural networks were trained faster with GPUs.

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Marktechpost

MARCH 23, 2024

To reduce the memory footprint and further speed up the training of FeatUp’s implicit network, the spatially varying features are compressed to their top k=128 principal components. Check out the Paper and MIT Blog. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter.

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

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Pickl AI

AUGUST 23, 2024

Summary: This blog post delves into the importance of explainability and interpretability in AI, covering definitions, challenges, techniques, tools, applications, best practices, and future trends. It highlights the significance of transparency and accountability in AI systems across various sectors.

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Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. In a typical neural network, you flatten your input one vector, take those input values in at once, multiply them by the weights in the first layer, add the bias, and pass the result into a neuron.

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IBM Journey to AI blog

AUGUST 9, 2024

Generative adversarial networks (GANs) consist of two neural networks: a generator that produces new content and a discriminator that evaluates the accuracy and quality of the generated content. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics.

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Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

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Towards AI

OCTOBER 3, 2024

In this blog, we’ll explore emerging frontiers that promise to overcome these limitations, highlight what has been achieved so far, delve into the promising research, and discuss the ongoing challenges that researchers are tackling to bring these innovations to fruition. Liquid AI (started at MIT CSAIL) is working on Liquid Neural Networks.–

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. In this blog, we’ll dive into the need for AI explainability, the various methods available currently, and their applications. Why do we need Explainable AI (XAI)?

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ML Review

MARCH 31, 2019

Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3%

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. 21 million ops) gets reduced by a factor of ~11.

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Marktechpost

NOVEMBER 30, 2023

Researchers from Lund University and Halmstad University conducted a review on explainable AI in poverty estimation through satellite imagery and deep machine learning. The review underscores the significance of explainability for wider dissemination and acceptance within the development community.

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Heartbeat

SEPTEMBER 13, 2023

Source: ResearchGate Explainability refers to the ability to understand and evaluate the decisions and reasoning underlying the predictions from AI models (Castillo, 2021). Explainability techniques aim to reveal the inner workings of AI systems by offering insights into their predictions. What is Explainability?

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Viso.ai

JULY 9, 2024

One unified infrastructure to build deploy scale secure real-world computer vision Get started Brief Introduction to GANs (Generative Adversarial Networks) GANs are made of two neural networks: A generator that creates new data A discriminator evaluates whether the generated data is real or fake.

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Mlearning.ai

FEBRUARY 24, 2023

In this blog post, we will explain what PCA is and how it works and present five examples of PCA analysis. This is done by finding the directions (the principal components) that explain the most variance in the data set. The eigenvalues represent the amount of variance explained by the corresponding eigenvector.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

“AI could lead to more accurate and timely predictions, especially for spotting diseases early,” he explains, “and it could help cut down on carbon footprints and environmental impact by improving how we use energy and resources.” We get tired, lose our focus, or just physically can’t see all that we need to.

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Towards AI

DECEMBER 19, 2023

For example, a deep neural net used for a loan application scorecard might deny a customer, and we will not be able to explain why. This is where an explainable neural network based on generalized additive models with structured interactions (GAMI-Net) comes into the picture. So, without further ado, let’s dive in!

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Towards AI

NOVEMBER 16, 2024

This blog introduces Diffusion Models, focusing on their intuition and mathematics for understanding related research. While much existing literature explains these models from a Markov Chain perspective, alternative perspectives and conditioning methods during generation remain underexplored. Markov Chain Perspective2.

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Towards AI

FEBRUARY 16, 2024

Machine learning algorithms, especially neural networks, are effective at identifying complex patterns in chemical data, which can lead to new insights and speed up processes that were previously dependent on traditional, more time-consuming methods. Join thousands of data leaders on the AI newsletter.

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Pickl AI

SEPTEMBER 23, 2024

Gain insights into neural networks, optimisation methods, and troubleshooting tips to excel in Deep Learning interviews and showcase your expertise. This blog will equip you with the most important Deep Learning interview questions, ranging from basic to advanced and scenario-based inquiries. What is a Neural Network?

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Towards AI

MARCH 29, 2024

It covers a wide range of topics, from optimizing Neural Networks for a particular hardware platform to new fabrication methods for building new hardware for AI applications. In the first one, Robert Dimond from ARM presented their chiplet… Read the full blog for free on Medium. The DATE 2024 conference programme.

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Bugra Akyildiz

SEPTEMBER 1, 2024

Before getting started, the phrase of “mechanistic interpretability(MI)” refers to the internal workings of neural networks by examining how specific features are represented and processed. Antropic wrote about various ideas from interpretability team in a blog post. It is mouthful, but simple term.

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NVIDIA

DECEMBER 4, 2023

Three technical reasons, and many stories, explain why that’s so. A RedHat software engineering team put it succinctly in a blog : “GPUs have become the foundation of artificial intelligence.” An AI model, also called a neural network, is essentially a mathematical lasagna, made from layer upon layer of linear algebra equations.

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IBM Journey to AI blog

AUGUST 13, 2024

Where it all started During the second half of the 20 th century, IBM researchers used popular games such as checkers and backgammon to train some of the earliest neural networks, developing technologies that would become the basis for 21 st -century AI. Elevate your AI with IBM watsonx. Train, deploy and scale models effortlessly.

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Pickl AI

SEPTEMBER 17, 2024

Summary: This blog covers 15 crucial artificial intelligence interview questions, ranging from fundamental concepts to advanced techniques. In this blog post, we will explore 15 essential artificial intelligence interview questions that cover a range of topics, from fundamental principles to cutting-edge techniques.

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IBM Journey to AI blog

JANUARY 15, 2024

This technology excels at applying foundation models, which are large neural networks trained on extensive unlabeled data and fine-tuned for various tasks. Learn more about AI can transform government services The post 5 key areas for governments to responsibly deploy generative AI appeared first on IBM Blog.

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Towards AI

AUGUST 11, 2023

IMAGE SOURCE: BY AUTHOR INTRODUCTION: GANs, Generative Adversarial networks, let’s first understand what GANs are. So I have already written a blog on Generative AI where we discuss many types of AI, the hype of AI, and why AI is tending nowadays in every sector like Healthcare, Finance, Trading, marketing, and many more.

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Towards AI

OCTOBER 8, 2023

This is exactly what this blog post is intended for. We’ll start from neural networks, brush up on deep learning, figure out what the heck is NLP, and eventually, after the not-so-painstaking process, learn about the working of large language models. What are neural networks? Fret no more! What is Machine Learning?

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IBM Journey to AI blog

JANUARY 10, 2024

AI can also work from deep learning algorithms, a subset of ML that uses multi-layered artificial neural networks (ANNs)—hence the “deep” descriptor—to model high-level abstractions within big data infrastructures.

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Towards AI

JULY 22, 2023

So, don’t worry, this is where Explainable AI, also known as XAI, comes in. In this blog, we’ll get headlong into XAI theory, expose its nuts and bolts, investigate numerous methodologies, and, of course, offer a variety of relevant and interesting examples that will make the magic of XAI crystal evident! SOURCE: [link] A.

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Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

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