Analytics Vidhya

MAY 3, 2024

50 Billion AGI Gamble Explained! His vision represents a significant shift in the development of AI technologies, emphasizing the transformative potential of AGI across various societal sectors. Altman has […] The post Is Sam Altman Crazy or a Genius? $50 appeared first on Analytics Vidhya.

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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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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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Analytics Vidhya

JANUARY 20, 2021

But using the process explained below will ease it out. The post A Quick Guide to Setting up a Virtual Environment for Machine Learning and Deep Learning on macOS appeared first on Analytics Vidhya. ArticleVideos Introduction Upgrading either Anaconda or Python on macOS is complicated. For this, I’m.

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Analytics Vidhya

NOVEMBER 29, 2021

In this article, we will learn about model explainability and the different ways to interpret a machine learning model. What is Model Explainability? Model explainability refers to the concept of being able to understand the machine learning model. For example – If a healthcare […].

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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

FEBRUARY 24, 2024

State-of-the-art approaches for CMRI segmentation have predominantly concentrated on SAX segmentation using deep learning methods like UNet. Join our 37k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Analytics Vidhya

MAY 14, 2023

Thus, understanding the disparity between two fundamental algorithms, Regression vs Classification, becomes essential. […] The post Regression vs Classification in Machine Learning Explained! appeared first on Analytics Vidhya.

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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. Luckily, there’s a handy tool to pick up Deep Learning Architecture.

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NYU Center for Data Science

NOVEMBER 9, 2023

Yann LeCun is a renowned deep learning pioneer and one of the most important minds in AI, and over the past few years he has been developing a comprehensive theory of machine learning, centered around “energy-based models,” which he calls “the only way to formalize and understand all model types.”

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Marktechpost

DECEMBER 22, 2023

Recent advancements led a team of scientists to develop a novel approach utilizing deep learning, a computer program capable of learning patterns and making predictions. Notably, the distinguishing feature of this approach is its transparency; the program can explain its decisions rather than operating as an opaque black box.

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Marktechpost

MARCH 31, 2024

Solving partial differential equations (PDEs) is complex, just like the events they explain. Deep learning, using designs like U-Nets, is popular for working with information at multiple levels of detail. Earlier methods of solving these equations struggled with the challenge of changes happening over time.

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Marktechpost

JANUARY 28, 2024

A researcher from the University of Zurich has turned to deep learning as a potent tool. Deep learning models, such as multilayer perceptrons, recurrent neural networks, and transformers, have been employed to forecast the fitness of genotypes based on experimental data. If you like our work, you will love our newsletter.

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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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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

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Overfitting: When a machine learning model excels on the training data but fails to generalize to new data, overfitting has taken place. What is Deep Learning?

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NYU Center for Data Science

JANUARY 18, 2024

Micah Goldblum , a Postdoctoral Researcher at CDS, has created exactly that, in a recent survey intended to capture the multifaceted views of influential figures in deep learning. Goldblum’s work, the first in a planned series, aims to document diverse opinions in the field, particularly those not amplified by social media platforms.

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Marktechpost

OCTOBER 8, 2023

The researchers emphasize that this approach of explainability examines an AI’s full prediction process from input to output. Dr. Sebastian Lapuschkin, head of the research group Explainable Artificial Intelligence at Fraunhofer HHI, explains the new technique in more detail. We are also on WhatsApp.

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Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. " Nuclear Engineering and Technology 53, no.

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PyImageSearch

JANUARY 30, 2023

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. As soon as the system adapts to human wants, it automates the learning process accordingly.

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Heartbeat

NOVEMBER 29, 2023

Two names stand out prominently in the wide realm of deep learning: TensorFlow and PyTorch. These strong frameworks have changed the field, allowing researchers and practitioners to create and deploy cutting-edge machine learning models. TensorFlow and PyTorch present distinct routes to traverse.

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Heartbeat

OCTOBER 18, 2023

In this article, we embark on a journey to explore the transformative potential of deep learning in revolutionizing recommender systems. However, deep learning has opened new horizons, allowing recommendation engines to unravel intricate patterns, uncover latent preferences, and provide accurate suggestions at scale.

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ODSC - Open Data Science

OCTOBER 17, 2023

But without a strong understanding of deep learning, you’ll have a difficult time getting the most out of the cutting-edge developments in the industry. At ODSC West this October 30th to November 2nd, you’ll build the core knowledge and skills you need with the sessions in the deep learning track , such as the ones listed below.

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Heartbeat

AUGUST 1, 2023

A World of Computer Vision Outside of Deep Learning Photo by Museums Victoria on Unsplash IBM defines computer vision as “a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs [1].”

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. In this post, we will look at deep learning approaches for time series analysis and how they might be used in real-world applications. Let’s dive in!

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Marktechpost

DECEMBER 15, 2023

They explained that the higher resolution of precipitation events simulated with this method will allow for a better estimation of the impacts the weather conditions that caused the flooding of the river Ahr in 2021 would have had in a world warmer by 2 degrees. If you like our work, you will love our newsletter.

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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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Mlearning.ai

JANUARY 3, 2024

word2vec dl4ee: deep learning for electrical engineers Why another article for word2vec? We will use the simplest concept of Linear Algebra, and Stochastic Process to explain it. However, the approximation can further reduce the computation complexity without losing its fidelity in the deep learning models later.

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Mlearning.ai

AUGUST 11, 2023

Introduction GPUs as main accelerators for deep learning training tasks suffer from under-utilization. Authors of AntMan [1] propose a deep learning infrastructure, which is a co-design of cluster schedulers (e.g., with deep learning frameworks (e.g., with deep learning frameworks (e.g.,

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Viso.ai

FEBRUARY 8, 2024

introduced deep belief networks (DBNs) in 2006. These deep learning algorithms consist of latent variables and use them to learn underlying patterns within the data. Deep belief networks played a vital role in the growth and evolution of deep learning, but they are rarely ever used today.

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Towards AI

JULY 17, 2023

About this series In this series, we will learn how to code the must-to-know deep learning algorithms such as convolutions, backpropagation, activation functions, optimizers, deep neural networks, and so on using only plain and modern C++. The problem often is: how to know the F() formula?

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Google Research AI blog

MARCH 14, 2023

We describe this feature and evaluate its use by pathologists in our recently published paper, “ Pathologist validation of a machine-learned feature for colon cancer risk stratification ”. Putting things in context: learning from deep learning as a paradigm Our work is an example of people “learning from deep learning”.

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Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

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Heartbeat

OCTOBER 3, 2023

Conclusion This is the end of the two-part series on memory mapping, where I have given a comprehensive beginner tutorial on the best method for a deep learning dive. These methods are not limited to deep learning as we have acquired an understanding of binaries and how they could be used to store information.

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PyImageSearch

JANUARY 2, 2023

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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