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Five machine learning types to know

IBM Journey to AI blog

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

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Everything you should know about AI models

Dataconomy

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? In March of 2022, DeepMind released Chinchilla AI.

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Everything you should know about AI models

Dataconomy

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? In March of 2022, DeepMind released Chinchilla AI.

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Debugging data to build better and more fair ML applications

Snorkel AI

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.

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Debugging data to build better and more fair ML applications

Snorkel AI

He presented “Building Machine Learning Systems for the Era of Data-Centric AI” at Snorkel AI’s The Future of Data-Centric AI event in 2022. The talk explored Zhang’s work on how debugging data can lead to more accurate and more fair ML applications. A transcript of the talk follows.

ML 52
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Retell a Paper: “Self-supervised Learning in Remote Sensing: A Review”

Mlearning.ai

2022’s paper. 2022 Deep learning notoriously needs a lot of data in training. 2022 Figure 3. 2022 Figure 4. 2022 for further reference. Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. Image: Wang et al., Taxonomy of SSL. Source: Wang et al.,

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. And this work appeared in AAAI 2022. The following is a transcript of his presentation, edited lightly for readability. AB : Got it.