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- Machine Learning - IBM Research
Machine learning uses data to teach AI systems to imitate the way that humans learn They can find the signal in the noise of big data, helping businesses improve their operations We’ve been in the field since since the beginning: IBMer Arthur Samuel even coined the term “Machine Learning” back in 1959
- Quantum Machine Learning: An Interplay Between Quantum Computing and . . .
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning It seeks to revolutionize machine learning by harnessing the unique capabilities of quantum mechanics and employs machine learning techniques to advance quantum computing research This paper presents an overview of quantum computing for the machine learning
- Introducing AI Fairness 360 - IBM Research
We are pleased to announce AI Fairness 360 (AIF360), a comprehensive open-source toolkit of metrics to check for unwanted bias in datasets and machine learning models, and state-of-the-art algorithms to mitigate such bias We invite you to use it and contribute to it to help engender trust in AI and make the world more equitable for all
- Introducing AI Explainability 360 - IBM Research
Introducing AI Explainability 360 We are pleased to announce AI Explainability 360, a comprehensive open source toolkit of state-of-the-art algorithms that support the interpretability and explainability of machine learning models We invite you to use it and contribute to it to help advance the theory and practice of responsible and
- What is AI inferencing? - IBM Research
Part of the Linux Foundation, PyTorch is a machine-learning framework that ties together software and hardware to let users run AI workloads in the hybrid cloud One of PyTorch’s key advantages is that it can run AI models on any hardware backend: GPUs, TPUs, IBM AIUs, and traditional CPUs
- What are foundation models? - IBM Research
What makes these new systems foundation models is that they, as the name suggests, can be the foundation for many applications of the AI model Using self-supervised learning and transfer learning, the model can apply information it’s learnt about one situation to another
- Snap machine learning - IBM Research
Optimizing Machine Learning Accelerate popular Machine Learning algorithms through system awareness, and hardware software differentiation Develop novel Machine Learning algorithms with best-in-class accuracy for business-focused applications AI in Business – Challenges Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models Such
- Systematic literature review: Quantum machine learning and its . . .
The main types of found algorithms are quantum implementations of classical machine learning algorithms, such as support vector machines or the k-nearest neighbor model, and classical deep learning algorithms, like quantum neural networks One of the most relevant applications in the machine learning field is image classification
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