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Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.
Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Delves into the challenges and benefits of deep learning, highlighting the transition to convolutional neural networks and the impact of network width on the loss landscape.
Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.