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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Towards AI

FEBRUARY 16, 2024

Image adapted from Adobe Stock Machine learning is becoming a significant tool in the field of chemistry, providing new opportunities in various areas such as drug discovery and materials science. Recently, the combination of machine learning and chemistry has made significant progress.

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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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NYU Center for Data Science

JANUARY 31, 2024

The idea that neural networks undergo distinct developmental phases during training has long been a subject of debate and fascination. Published in the Transactions on Machine Learning Research (TMLR) , Hu’s work offers new insights into the training dynamics of neural networks.

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Towards AI

APRIL 8, 2024

Interconnected graphical data is all around us, ranging from molecular structures to social networks and design structures of cities. Graph Neural Networks (GNNs) are emerging as a powerful method of modeling and learning the spatial and graphical structure of such data. An illustration of GNN: Figure 1.

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Marktechpost

SEPTEMBER 30, 2023

This redundancy consumes extra energy and leads to an expanded chip footprint, raising concerns about space efficiency and scalability in large-scale optical neural networks (ONNs) and optimization problem solvers. Efforts to address this issue have been made, with solutions such as a pseudo-real-value MZI mesh.

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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.

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Marktechpost

JANUARY 19, 2024

Epigenetic clocks estimate chronological age using supervised machine learning and CpG combinations. To conclude, the researchers have introduced a precise and interpretable neural network architecture based on DNA methylation for age estimation. The most noteworthy result was probably obtained for the pan-tissue dataset.

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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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Marktechpost

MARCH 11, 2024

However, understanding their information-flow dynamics, learning mechanisms, and interoperability remains challenging, limiting their applicability in sensitive domains requiring explainability. These matrices are leveraged to develop class-agnostic and class-specific tools for explainable AI of Mamba models.

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Marktechpost

JANUARY 15, 2024

Normative models explain why people should make certain decisions based on principles, while explanatory models aim to capture how people decide. One recent study claims to have found a more accurate model for predicting human decisions using neural networks trained on a large online dataset called choices13k.

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IBM Journey to AI blog

OCTOBER 16, 2023

Machine learning (ML)—the artificial intelligence (AI) subfield in which machines learn from datasets and past experiences by recognizing patterns and generating predictions—is a $21 billion global industry projected to become a $209 billion industry by 2029.

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Towards AI

MARCH 17, 2024

Alongside this, there is a second boom in XAI or Explainable AI. Explainable AI is focused on helping us poor, computationally inefficient humans understand how AI “thinks.” We will then explore some techniques for building glass-box or explainable models. Ultimately these definitions end up being almost circular!

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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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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Marktechpost

APRIL 26, 2024

Notable examples include CrossBeam, which leverages execution states in sequence-to-sequence models, and specialized neural architectures like the instruction pointer attention graph neural networks. If you like our work, you will love our newsletter.

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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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Unite.AI

OCTOBER 5, 2023

I've so far read over 60 books on AI, and while some of them do get repetitive, this book managed to offer a fresh perspective, I enjoyed this book enough to add it to my personal list of the Best Machine Learning & AI Books of All Time. AI as neural networks is merely (!) Different target audience.

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AI News

DECEMBER 18, 2023

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular. They then analyse and assess risks to ensure compliance with regulations.

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Viso.ai

MAY 10, 2024

Whether you’re building a consumer app to recognize plant species or an enterprise tool to monitor office security camera footage, you are going to need to build a Machine Learning (ML) model to provide the core functionality. Building a machine learning model consists of 7 high-level steps: 1.

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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. What is Machine Learning? Machine learning algorithms can make predictions or classifications based on input data.

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Mlearning.ai

OCTOBER 2, 2023

Learn how to use them to avoid the biggest scare in ML: overfitting and underfitting. Photo by Arseny Togulev on Unsplash If you’re working with a dataset and trying to build a machine learning model, you probably don’t need all the data and columns that feed into your model. Here’s the overview.

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AI News

DECEMBER 15, 2023

In an interview at AI & Big Data Expo , Alessandro Grande, Head of Product at Edge Impulse , discussed issues around developing machine learning models for resource-constrained edge devices and how to overcome them. “A lot of the companies building edge devices are not very familiar with machine learning,” says Grande.

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Marktechpost

SEPTEMBER 26, 2023

Microsoft researchers propose a groundbreaking solution to these challenges in their recent “Neural Graphical Models” paper presented at the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023).

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PyImageSearch

MAY 8, 2023

Home Table of Contents Introduction to Causality in Machine Learning Correlation and Causation Case Study 1: A “Marvelous” Problem Scenario 1: A Direct Cause Scenario 2: Reversing the Cause and Effect Scenario 3: Investigating a Hidden Cause Causal Thinking Case Study 2: Food App Conundrum Quiz Time! Let’s find out.

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Marktechpost

OCTOBER 8, 2023

The field of Machine Learning and Artificial Intelligence has become very important. By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. We have new advancements that have been there with each day. Consequently, Prof.

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Marktechpost

APRIL 28, 2024

and also allows the students to build an understanding of machine learning algorithms, including supervised, unsupervised, reinforcement, etc. The course covers the common terminologies of AI, including neural networks, machine learning, deep learning, etc., and what AI can and cannot do.

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Marktechpost

JULY 2, 2023

Thus, there is a growing demand for explainability methods to interpret decisions made by modern machine learning models, particularly neural networks. This innovative strategy enables a comprehensive understanding of the underlying components within the neural network.

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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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Marktechpost

JULY 6, 2023

As a result of recent technological advances in machine learning (ML), ML models are now being used in a variety of fields to improve performance and eliminate the need for human labor. This is why when Explainable AI models can give a clear idea of why a decision was made but not how it arrived at that decision.

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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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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

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

JULY 22, 2023

This is similar to how machine learning (ML) can seem at first. Our job is to explain complicated tech terms in a simple day-to-day language, so everyone can understand. In today’s post, we’re going to decode ten of the most common machine learning terms. But don’t worry! That’s why we’re here.

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Unite.AI

JUNE 25, 2023

Hybrid Approach for Physics-Aware AI Traditionally, computer vision, the field that enables AI to comprehend and infer properties of the physical world from images, has largely focused on data-based machine learning. However, assimilating the understanding of physics into the realm of neural networks has proved challenging.

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Viso.ai

FEBRUARY 8, 2024

Restricted Boltzmann Machines A Restricted Boltzmann Machine is a two-layer probabilistic neural network. Its first layer (visible layer) interacts with the raw data, and the second (hidden layer) learns high-level features from the first one. Book a demo to learn more about the Viso suite.

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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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