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. Today, we talk about TDA, which aims to relate a model’s inference from a specific sample to its training data.

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Unite.AI

FEBRUARY 25, 2025

The company has built a cloud-scale automated reasoning system, enabling organizations to harness mathematical logic for AI reasoning. With a strong emphasis on developing trustworthy and explainable AI , Imandras technology is relied upon by researchers, corporations, and government agencies worldwide.

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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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ODSC - Open Data Science

MARCH 10, 2025

Training Sessions Bayesian Analysis of Survey Data: Practical Modeling withPyMC Allen Downey, PhD, Principal Data Scientist at PyMCLabs Alexander Fengler, Postdoctoral Researcher at Brown University Bayesian methods offer a flexible and powerful approach to regression modeling, and PyMC is the go-to library for Bayesian inference in Python.

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Pickl AI

JANUARY 12, 2025

For instance, in retail, AI models can be generated using customer data to offer real-time personalised experiences and drive higher customer engagement, consequently resulting in more sales. Aggregated, these methods will illustrate how data-driven, explainable AI empowers businesses to improve efficiency and unlock new growth paths.

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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ODSC - Open Data Science

DECEMBER 7, 2023

We’ll also discuss some of the benefits of using set union(), and we’ll see why it’s a popular tool for Python developers. We’ll also discuss some of the benefits of using set union(), and we’ll see why it’s a popular tool for Python developers.

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Towards AI

JULY 24, 2024

Well, get ready because we’re about to embark on another exciting exploration of explainable AI, this time focusing on Generative AI. Before we dive into the world of explainability in GenAI, it’s worth noting that the tone of this article, like its predecessor, is intentionally casual and approachable.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Sweetviz GitHub | Website Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. These tools will help make your initial data exploration process easy. Output is a fully self-contained HTML application.

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Marktechpost

JUNE 28, 2024

Image Source OpenDevin: This open-source project aims to create an autonomous AI software engineer to handle complex engineering tasks and collaborate with users. OpenDevin exemplifies how AI can democratize software development. This system highlights the potential of AI to manage dynamic and evolving objectives efficiently.

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IBM Journey to AI blog

AUGUST 11, 2023

Using AutoML or AutoAI, opensource libraries such as scikit-learn and hyperopt, or hand coding in Python, ML engineers create and train the ML models. Build and train models—Here is where ML teams use Ops practices to make MLOps. In short, they’re using existing ML training models to train new models for business applications.

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IBM Journey to AI blog

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. to learn more) In other words, you get the ability to operationalize data science models on any cloud while instilling trust in AI outcomes.

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Mlearning.ai

FEBRUARY 7, 2023

It can write, explain, and correct code in many major programming languages (such as Python and JavaScript), data formats (such as HTML, JSON, XML, and CSV) and other structured languages like SQL. 1x: A nice prompt forcing the AI to interrupt itself while explaining AI alignment.

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AWS Machine Learning Blog

APRIL 12, 2023

We could re-use the previous Sagemaker Python SDK code to run the modules individually into Sagemaker Pipeline SDK based runs. Script mode allowed us to have minimal changes in our training code, and the SageMaker pre-built Docker container handles the Python, Framework versions, and so on.

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Marktechpost

DECEMBER 6, 2024

This step-by-step reasoning builds trust in its outputs and facilitates seamless integration into applications requiring clear and explainable AI logic. Its technical ecosystem is built on widely used frameworks such as Python, PyTorch, and the Transformers library, allowing developers compatibility and ease of use.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Machine Learning with XGBoost Matt Harrison | Python & Data Science Corporate Trainer | Consultant | MetaSnake Join one of the leading experts in Python for this upcoming ODSC East session. By the end, you will be ready to harness the platform for advanced spatial analysis and the development of sophisticated AI models.

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ODSC - Open Data Science

SEPTEMBER 26, 2024

Using AI to Detect Anomalies in Robotics at the Edge Integrating AI-driven anomaly detection for edge robotics can transform countless industries by enhancing operational efficiency and improving safety. Where do explainable AI models come into play?

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ODSC - Open Data Science

JUNE 8, 2023

Some of our most popular in-person sessions were: Data Science Software Acceleration at the Edge: Audrey Reznik Guidera | Sr.

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Dlabs.ai

MAY 22, 2022

Machine Learning with Python: A Practical Introduction Author: Saeed Aghabozorgi Ph.D. According to GitHub , Python is the most popular programming language used in machine learning. That’s why you should consider learning how to apply it in ML projects, and this Machine Learning in Python course can help you with that.

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Pickl AI

NOVEMBER 5, 2024

Lack of Transparency Many AI systems operate as “black boxes,” making it difficult for users to understand how decisions are made. Explainable AI (XAI) is crucial for building trust in automated systems. Explainable AI (XAI) There is a growing demand for transparency in AI decision-making processes.

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The MLOps Blog

JUNE 27, 2023

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., Some popular hyperparameter optimization MLOps tools in 2023 Optuna Optuna is an open-source hyperparameter optimization framework in Python. and Pandas or Apache Spark DataFrames.

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ODSC - Open Data Science

JUNE 1, 2023

Real-Time ML with Spark and SBERT, AI Coding Assistants, Data Lake Vendors, and ODSC East Highlights Getting Up to Speed on Real-Time Machine Learning with Spark and SBERT Learn more about real-time machine learning by using this approach that uses Apache Spark and SBERT. Is an AI Coding Assistant Right For You?

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Pickl AI

SEPTEMBER 3, 2024

Explainable AI As ANNs are increasingly used in critical applications, such as healthcare and finance, the need for transparency and interpretability has become paramount. Familiarity with libraries and frameworks like TensorFlow, Keras, and PyTorch can significantly enhance productivity.

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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. Captum allows users to explain both deep learning and traditional machine learning models.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Upload the datasets mammo-batch-dataset.csv and mammo-batch-dataset-outliers.csv to the prefix mammography-severity-model/batch-dataset of the S3 bucket in the prod account. resource('s3') s3_client.Bucket(default_bucket).upload_file("pipelines/train/scripts/raw_preprocess.py","mammography-severity-model/scripts/raw_preprocess.py")

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Pickl AI

JANUARY 13, 2025

Using simple language, it explains how to perform data analysis and pattern recognition with Python and R. Practical examples using Python and R. Explains real-world applications like fraud detection. Includes Python-based coding exercises. Explains underlying mathematical concepts.

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Bugra Akyildiz

MAY 25, 2024

Data Tasks ChatGPT can handle a wide range of data-related tasks by writing and executing Python code behind the scenes, without users needing coding expertise. ChatGPT would understand the intent behind the query and translate it into the appropriate SQL or Python code to execute against databases or data warehouses.

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Pickl AI

AUGUST 1, 2024

In addition to these frameworks, Deep Learning engineers often use programming languages like Python and R, along with libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualisation. Proficiency in programming languages like Python, experience with Deep Learning frameworks (e.g.,

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Pickl AI

JANUARY 29, 2024

AI comprises Natural Language Processing, computer vision, and robotics. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial. Emerging Trends Emerging trends in Data Science include integrating AI technologies and the rise of Explainable AI for transparent decision-making.

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Pickl AI

MAY 15, 2024

Machine Learning Explainability (ML Explainability) Frameworks Frameworks like DARPA’s Explainable AI (XAI) toolkit or LIME (Local Interpretable Model-Agnostic Explanations) provide tools and techniques for understanding how models arrive at predictions.

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Pickl AI

AUGUST 14, 2024

Explainable AI (XAI): Efforts to make neural networks more interpretable, allowing users to understand how models make decisions. Scikit-learn : A versatile library for Machine Learning in Python, providing tools for data preprocessing and model evaluation.

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Pickl AI

APRIL 2, 2024

Tools and Technologies Python/R: Popular programming languages for data analysis and machine learning. Explainable AI (XAI): As AI models become more complex, there’s a growing need for interpretability. Unsupervised Learning: Finding patterns or insights from unlabeled data.

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Pickl AI

NOVEMBER 18, 2024

Here are some key components to consider: Programming Languages Two of the most widely used programming languages for Machine Learning are Python and R. Python’s simplicity and vast ecosystem of libraries make it the go-to choice for both beginners and professionals. Let’s explore some of the key trends.

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Viso.ai

APRIL 25, 2024

Deep learning models are black-box methods by nature, and even though those models succeeded the most in CV tasks, explainability is still poorly assessed. Explainable AI improves the transparency of those models making them more trustworthy. In this subsection, we will explore XAI as one advancement that helped bias detection.

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Marktechpost

JULY 11, 2023

Auto-GPT, the free-of-cost and open-source in nature Python application, uses GPT-4 technology. Stacking is an approach that lets AI models use other models as tools or mediums to accomplish a task. Unlike the previous version, GPT 3.5, AutoGPT uses the concept of stacking to recursively call itself.

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Heartbeat

SEPTEMBER 7, 2023

Let’s see it in action with some Python code: import comet_ml # Initialize Comet.ml It’s like having a virtual laboratory where every experiment is meticulously logged and displayed. Imagine you’re training a deep learning model for image recognition. With Comet, you can easily log and visualize metrics during training.

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The MLOps Blog

FEBRUARY 27, 2025

Transparency has become a key expectation in the AI industry, as highlighted by initiatives like the EU AI Act and guidelines from organizations such as the Partnership on AI, which emphasize the importance of explainable AI. Specifically, it measures whether the probability of a positive outcome (e.g.,

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