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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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

MAY 5, 2024

Existing surveys detail a range of techniques utilized in Explainable AI analyses and their applications within NLP. They explore methods to decode information in neural network models, especially in natural language processing. Recent approaches automate circuit discovery, enhancing interpretability.

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Marktechpost

MAY 12, 2024

Greip Greip is an AI-powered fraud protection tool that assists developers in protecting their app’s financial security by avoiding payment fraud. Greip provides an AI-powered fraud protection solution that utilizes ML modules to validate each transaction in an app and assess the possibility of fraudulent behavior.

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Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

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Pickl AI

SEPTEMBER 17, 2024

Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. What Is the Purpose of The Activation Function in Artificial Neural Networks?

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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. Moreover, it can compute these contribution scores efficiently in just one backward pass through the network.

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IBM Journey to AI blog

JULY 6, 2023

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Some examples of data science use cases include: An international bank uses ML-powered credit risk models to deliver faster loans over a mobile app. What is machine learning?

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Viso.ai

JULY 9, 2024

On the other hand, the generative AI task is to create new data points that look like the existing ones. Discriminative models include a wide range of models, like Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), or even simpler models like random forests.

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Defined.ai blog

AUGUST 10, 2023

Understanding the AI-ML Connection in Financial Data Analysis Artificial Intelligence and Machine Learning (ML) often come hand in hand when discussing advanced technology. AI refers to computer systems capable of executing tasks that typically require human intelligence.

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Marktechpost

AUGUST 24, 2023

Here’s our curated list of the top AI and Machine Learning-related subreddits to follow in 2023 to keep you in the loop. million members, this is a must-join group for ML enthusiasts. r/artificial r/artificial is the largest subreddit dedicated to all issues related to Artificial Intelligence or AI. With over 2.5

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Marktechpost

JUNE 9, 2024

Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features. xECGArch uniquely separates short-term (morphological) and long-term (rhythmic) ECG features using two independent Convolutional Neural Networks CNNs.

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Dlabs.ai

MAY 22, 2022

That shows how companies are increasingly investing in ML solutions, often looking for skilled professionals to help them create custom software. Given the data, it’s little surprise that many people want to learn more about AI and ML and, in turn, develop the necessary skills to become a machine learning engineer.

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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. Today, building an ML model is easier than ever before using frameworks like Tensorflow.

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Marktechpost

OCTOBER 15, 2024

XAI, or Explainable AI, brings about a paradigm shift in neural networks that emphasizes the need to explain the decision-making processes of neural networks, which are well-known black boxes. Don’t Forget to join our 50k+ ML SubReddit. If you like our work, you will love our newsletter.

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Heartbeat

NOVEMBER 27, 2023

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. Deep learning automates and improves medical picture analysis.

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Heartbeat

SEPTEMBER 13, 2023

Distinction Between Interpretability and Explainability Interpretability and explainability are interchangeable concepts in machine learning and artificial intelligence because they share a similar goal of explaining AI predictions. Explaining Explanations in AI || SSRN Onose, E. Russell, C. &

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Unite.AI

APRIL 9, 2024

Composite AI is a cutting-edge approach to holistically tackling complex business problems. These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. Decision trees and rule-based models like CART and C4.5

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Mlearning.ai

FEBRUARY 6, 2023

Classifiers based on neural networks are known to be poorly calibrated outside of their training data [3]. There are plenty of techniques to help reduce overfitting in ML models. This is why we need Explainable AI (XAI). Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2021).

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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. Artificial Intelligence (AI) models assist across various domains, from regression-based forecasting models to complex object detection algorithms in deep learning.

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Viso.ai

MARCH 26, 2024

Initially, AI’s role in finance was limited to basic computational tasks. With advancements in machine learning (ML) and deep learning (DL), AI has begun to significantly influence financial operations. By controlling the entire lifecycle, ML teams no longer need to rely on point solutions to fill in the gaps.

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Pickl AI

JULY 1, 2024

Machine Learning Techniques for Demand Forecasting Machine Learning (ML) offers powerful tools for tackling complex demand forecasting challenges. Neural Networks Inspired by the human brain, artificial neural networks learn complex relationships within data for highly accurate demand forecasting, especially with vast datasets.

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How to Learn Machine Learning

JUNE 9, 2024

Healthcare organizations are using healthcare AI/ML solutions to achieve operational efficiency and deliver quality patient care. This continuous learning enables the ML systems to improve their outcomes and make better predictions on new data over time. This capability makes AutoML one of the popular ML trends of 2024.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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IBM Journey to AI blog

SEPTEMBER 20, 2023

Foundational models (FMs) are marking the beginning of a new era in machine learning (ML) and artificial intelligence (AI) , which is leading to faster development of AI that can be adapted to a wide range of downstream tasks and fine-tuned for an array of applications. What are large language models?

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Viso.ai

DECEMBER 6, 2023

State of Computer Vision Tasks in 2024 The field of computer vision today involves advanced AI algorithms and architectures, such as convolutional neural networks (CNNs) and vision transformers ( ViTs ), to process, analyze, and extract relevant patterns from visual data. Get a demo here.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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Bugra Akyildiz

MAY 25, 2024

There are also a variety of capabilities that can be very useful for ML/Data Science Practitioners for data related or feature related tasks. Users can interact with and customize these charts by hovering over elements, changing colors, and applying other formatting options. Are the internal representations in these systems also converging?

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Mlearning.ai

FEBRUARY 2, 2023

In this article, I show how a Convolutional Neural Network can be used to predict a person's age based on the person's ECG Attia et al 2019 [1], showed that a person's age could be predicted from an ECG using convolutional neural networks (CNN). Ismail Fawaz et al., Data Min Knowl Disc 34 , 1936–1962 (2020).

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How can we explain it in simple terms?

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IBM Journey to AI blog

SEPTEMBER 3, 2024

Bias Humans are innately biased, and the AI we develop can reflect our biases. These systems inadvertently learn biases that might be present in the training data and exhibited in the machine learning (ML) algorithms and deep learning models that underpin AI development. Take action: Adopt explainable AI techniques.

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