Marktechpost

APRIL 7, 2024

Global feature effects methods, such as Partial Dependence Plots (PDP) and SHAP Dependence Plots, have been commonly used to explain black-box models by showing the average effect of each feature on the model output. In conclusion, Effector offers a promising solution to the challenges of explainability in machine learning models.

Machine Learning 130

Machine Learning Python Explainability Explainable AI 130

Unite.AI

FEBRUARY 25, 2025

As AI systems increasingly power mission-critical applications across industries such as finance, defense, healthcare, and autonomous systems, the demand for trustworthy, explainable, and mathematically rigorous reasoning has never been higher. Raising the Bar in AI Reasoning Denis Ignatovich, Co-founder and Co-CEO of Imandra Inc.,

Automation 100

Automation Neural Network Explainability Algorithm 100

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. Quanda differs from its contemporaries, like Captum, TransformerLens, Alibi Explain, etc.,

Explainability 56

Explainability Explainable AI Python Neural Network 56

Marktechpost

AUGUST 24, 2024

EXplainable AI (XAI) has become a critical research domain since AI systems have progressed to being deployed in essential sectors such as health, finance, and criminal justice. The intrinsic complexity—the so-called “black boxes”—given by AI models makes research in the field of XAI difficult.

Python 107

Python Explainability Explainable AI AI 107

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.

Explainable AI 52

Explainable AI Explainability Deep Learning Neural Network 52

Pickl AI

JANUARY 12, 2025

Introduction Are you struggling to decide between data-driven practices and AI-driven strategies for your business? Besides, there is a balance between the precision of traditional data analysis and the innovative potential of explainable artificial intelligence.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Explainability Explainable AI 52

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.

Data Scientist 52

Data Scientist Data Science LLM Machine Learning 52

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.

Data Scientist 98

Data Scientist Explainable AI Data Science Python 98

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?

Machine Learning 52

Machine Learning Explainability Deep Learning Explainable AI 52

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?

Explainability 52

Explainability Machine Learning Data Scientist Deep Learning 52

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.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

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.

ChatGPT 105

ChatGPT Large Language Models Explainability Machine Learning 105

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.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

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.

Data Science 175

Data Science Machine Learning Big Data Data Mining 175

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., This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. and programmatically via the Kolena Python client.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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.

Metadata 98

Metadata Deep Learning ML Data Science 98

Pickl AI

JANUARY 13, 2025

Key Features: Comprehensive coverage of AI fundamentals and advanced topics. Explains search algorithms and game theory. Using simple language, it explains how to perform data analysis and pattern recognition with Python and R. Practical examples using Python and R. Explains reinforcement learning techniques.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Machine Learning 52

ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end, you will be ready to harness the platform for advanced spatial analysis and the development of sophisticated AI models.

Machine Learning 52

Machine Learning Data Science Python ML 52

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?

Robotics 52

Robotics Data Science Large Language Models Explainable AI 52

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.

Python 43

Python Explainable AI Explainability AI 43

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.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 52

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.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Machine Learning 52

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?

ML 52

ML Data Scientist Big Data Data Science 52

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.

Data Science 52

Data Science Machine Learning NLP Software Engineer 52

Dlabs.ai

MAY 22, 2022

The instructors are very good at explaining complex topics in an easy-to-understand way. Machine Learning Author: Andrew Ng Everyone interested in machine learning has heard of Andrew Ng : one of the most respected people in the AI world. Machine Learning with Python: A Practical Introduction Author: Saeed Aghabozorgi Ph.D.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Machine Learning Data Scientist 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

This post explained how to create an MLOps framework in a multi-environment setup to enable automated model retraining, batch inference, and monitoring with Amazon SageMaker Model Monitor, model versioning with SageMaker Model Registry, and promotion of ML code and pipelines across environments with a CI/CD pipeline.

Data Drift 119

Data Drift Auto-complete ML Automation 119

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.

ChatGPT 59

ChatGPT Machine Learning Data Analysis OpenAI 59

The MLOps Blog

FEBRUARY 27, 2025

A StereoSet prompt might be: “The software engineer was explaining the algorithm. How to integrate transparency, accountability, and explainability? Transparency is your ally : You dont have to explain every inner detail of your AI models to be transparent. Lets see how to use them in a simple example.

LLM 64

LLM Large Language Models Explainability Machine Learning 64

Viso.ai

APRIL 25, 2024

However, it is worth noting that even though this class imbalance has a significant impact, they do not explain every disparity in the performance of machine learning algorithms. 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.

Computer Vision 52

Computer Vision Categorization Algorithm Machine Learning 52

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.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

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.,

Deep Learning 52

Deep Learning Neural Network Machine Learning Natural Language Processing 52

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.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

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.

Machine Learning 52

Machine Learning Algorithm Data Quality Neural Network 52

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.

Auto-complete 40

Auto-complete Generative AI Large Language Models OpenAI 40

Mlearning.ai

FEBRUARY 2, 2023

This could for instance be used for patient risk stratification or maybe we could use eXplainable AI (XAI) to better understand these patterns and teach the doctors how to look for them. The development of these models (including Attia et al 2019) have mainly been done on patient data owned by the hospitals and not on open data.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Explainable AI 52

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.

Software Engineer 109

Software Engineer Software Development Automation Explainable AI 109

Pickl AI

MAY 15, 2024

Model Interpretability and Explainability While complex models might achieve high accuracy, it’s often challenging to interpret their decision-making processes. If you want to shape your Data Science career, you should sharpen your Python skills and explore libraries like NumPy, Pandas, Scikit-learn, and TensorFlow.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52