'Gradient descent learning rule' presentation slideshows

Multi Layer Perceptron

Multi Layer Perceptron. x 1. x 2. x n. Threshold Logic Unit (TLU). inputs. weights. w 1. output. activation. w 2. ?. y. . . . q. a= ? i=1 n w i x i. w n. 1 if a ? q y = 0 if a < q. {. Activation Functions. threshold. linear. y. y. a. a.

1.77k views • 85 slides

Multi Layer Perceptron

Multi Layer Perceptron. x 1. x 2. x n. Threshold Logic Unit (TLU). inputs. weights. w 1. output. activation. w 2. ?. y. . . . q. a= ? i=1 n w i x i. w n. 1 if a ? q y = 0 if a < q. {. Activation Functions. threshold. linear. y. y. a. a.

1.21k views • 85 slides

Foundations of Learning and Adaptive Systems ICS320

Foundations of Learning and Adaptive Systems ICS320. Part 4: Artificial Neural Networks. Outline. The Brain Perceptrons Gradient descent Multi-layer networks Backpropagation. Artificial Neural Networks. Other terms/names connectionist parallel distributed processing

796 views • 62 slides

Foundations of Learning and Adaptive Systems ICS320

Foundations of Learning and Adaptive Systems ICS320. Part 4: Artificial Neural Networks. Outline. The Brain Perceptrons Gradient descent Multi-layer networks Backpropagation. Artificial Neural Networks. Other terms/names connectionist parallel distributed processing

679 views • 62 slides

953 views • 58 slides

Longin Jan Latecki Temple University latecki@temple

779 views • 54 slides

CS 9633 Machine Learning

CS 9633 Machine Learning. Neural Networks. Adapted from notes by Tom Mitchell http://www-2.cs.cmu.edu/~tom/mlbook-chapter-slides.html. Neural networks. Practical method for learning Real-valued functions Discrete-valued functions Vector-valued functions Robust in the presence of noise

819 views • 44 slides

Data Mining with Neural Networks (HK: Chapter 7.5)

511 views • 33 slides

Data Mining with Neural Networks (HK: Chapter 7.5)

439 views • 33 slides

Information Theory and Learning

Information Theory and Learning. Tony Bell Helen Wills Neuroscience Institute University of California at Berkeley. One input, one output deterministic Infomax: match the input distribution to the non-linearity:. Gradient descent learning rule to maximise the transferred information.

304 views • 27 slides

Information Theory and Learning

Information Theory and Learning. Tony Bell Helen Wills Neuroscience Institute University of California at Berkeley. One input, one output deterministic Infomax: match the input distribution to the non-linearity:. Gradient descent learning rule to maximise the transferred information.

296 views • 27 slides