Neural Networks for Machine Learning [Hinton] - Printable Version +- MKLab (https://mklab.gr) +-- Forum: [INDEX] (https://mklab.gr/forumdisplay.php?fid=1) +--- Forum: ARTFICIAL INTELLIGENCE (AI) (https://mklab.gr/forumdisplay.php?fid=5) +---- Forum: COURSES-LECTURES (https://mklab.gr/forumdisplay.php?fid=38) +---- Thread: Neural Networks for Machine Learning [Hinton] (/showthread.php?tid=497)

Neural Networks for Machine Learning [Hinton] - mklabgr - 06-18-2026 Neural Networks for Machine Learning by [G.Hinton] Summary The page contains the lecture materials for Geoffrey Hinton’s 2012 Coursera course “Neural Networks for Machine Learning,” a foundational course on deep learning and artificial neural networks. It covers the history and principles of neural networks, neuron models, learning algorithms, perceptrons, backpropagation, gradient descent, convolutional neural networks, recurrent neural networks, LSTM, regularization methods, Bayesian learning, dropout, Boltzmann machines, deep belief networks, autoencoders, and representation learning.  The lectures aim to explain both the mathematical foundations and practical techniques behind modern machine learning systems, showing how neural networks learn patterns from data and how these ideas led to major advances in artificial intelligence. COURSE PAGE