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Image Recognition with Deep Learning and Neural Networks

Image Recognition with Deep Learning and Neural Networks

Learn about image recognition, Deep neural networks, how do they work, and explore some of the main use cases.

Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a. Supervised Learning Learns from labeled data. Goal: Predict output for new inputs. Common tasks: Classification, Regression. Examples: Linear Regression, Decision Trees, SVM. b. Unsupervised Learning Learns from unlabeled data. Goal: Discover hidden patterns. Common tasks: Clustering, Dimensionality Reduction. Examples: K-Means, PCA, DBSCAN. c. Semi-Supervised Learning Uses a small amount of labeled data large unlabeled data.

Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a.

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