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Jul 29, 2024 · A responsible machine learning workflow with focus on interpretable models, post-hoc explanation, and discrimination testing; On the art and Science of Explainable Machine Learning; Blog posts. How to test machine learning code and systems; How to Trust your Deep Learning Code; Model Assertions for Debugging Machine Learning
Sep 16, 2024 · What are AI Debugging Tools? AI debugging tools are software solutions that use artificial intelligence (AI) to automate identifying, diagnosing, and resolving bugs in code. They employ machine learning, natural language processing, and predictive analytics to detect anomalies, suggest fixes, and even self-heal code issues in real time.
Aug 27, 2024 · Debugging is a key part of software development, regardless of what type of software it is—so, naturally, it also applies to machine learning. In machine learning, poor model performance can have a wide range of causes, and debugging might take a lot of work. No predictive power or suboptimal values can cause models to perform poorly.
Nov 14, 2023 · Debugging neural network models can be a challenging task that might require profound understanding and experience in different areas of software development and machine learning techniques. Before designing a neural network solution, it is therefore important to have a well-defined strategy that will simplify model debugging.
May 29, 2023 · For instance, a study spearheaded by the University of Cambridge found that machine learning applied to bug detection could identify an astounding 97% of errors in the tested codes, significantly ...
Mar 26, 2024 · Most common use cases for AI and ML, sourced from statista.com. Machine learning model debugging is the process of identifying and resolving issues, or ‘bugs’, within machine learning models that can affect their performance. The Unique Challenges of ML Model Debugging. Debugging is a crucial part of the traditional software development ...
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Jan 11, 2024 · Let's delve into some notable case studies that shed light on the complexities of AI debugging. Case Study 1: Diagnosing Overfitting in a Predictive Model: A retail company developed a machine learning model to forecast future product demand based on historical sales data. However, the model's predictions weren’t aligning with actual outcomes.
