difference between supervised and unsupervised learning Machine learning vs deep learning CNN vs RNN

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Supervised learning uses labeled data for training, while unsupervised learning uses unlabeled data. Machine learning is a subset of AI, while deep learning is a subset of machine learning. CNNs are used for image recognition, while RNNs are used for sequential data.

Supervised learning requires labeled data for training, where the model learns to map input data to output labels (e.g., classification or regression tasks). Examples include linear regression, logistic regression, and support vector machines.
Unsupervised learning deals with unlabeled data, where the model learns patterns and relationships within the data without explicit output labels. Examples include clustering algorithms like K-means and hierarchical clustering.
Machine learning is a broader concept that involves algorithms that can learn from and make predictions or decisions based on data. Deep learning is a subset of machine learning that uses neural networks with multiple layers to learn complex patterns in large datasets.
Convolutional Neural Networks (CNNs) are commonly used in image recognition tasks due to their ability to capture spatial hierarchies in data. Recurrent Neural Networks (RNNs) are suitable for sequential data processing, such as natural language processing and time series analysis.

difference between supervised and unsupervised learning Machine learning vs deep learning CNN vs RNN

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