The Legacy of Geoffrey Hinton

Oct 21, 2024

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Geoffrey Hinton, the "Godfather of Deep Learning," revolutionized AI with the backpropagation algorithm and co-invented the Boltzmann Machine. His work on AlexNet and Google Brain drove major advancements in AI, including Capsule Networks. Awarded the Turing Award in 2018 and the Nobel Prize in Physics in 2024, Hinton's pioneering contributions continue to shape the future of artificial intelligence.

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The Legacy of Geoffrey Hinton

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Geoffrey Hinton

CSC321: Introduction to Neural Networks and Machine Learning Lecture 24: Non-linear Support Vector Machines. Geoffrey Hinton. The story so far. If we use a large set of non-adaptive features, we can often make the two classes linearly separable.

404 views • 12 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks. Geoffrey Hinton. Recurrent networks. A feed-forward neural net just computes a fixed sequence of non-linear learned transformations to convert an input patter into an output pattern.

592 views • 17 slides

Geoffrey Hinton

CSC321: Introduction to Neural Networks and Machine Learning Lecture 19: Learning Restricted Boltzmann Machines. Geoffrey Hinton. A simple learning module: A Restricted Boltzmann Machine. We restrict the connectivity to make learning easier. Only one layer of hidden units.

820 views • 30 slides

Geoffrey Hinton

CSC321: Neural Networks Lecture 13: Learning without a teacher: Autoencoders and Principal Components Analysis. Geoffrey Hinton. Three problems with backpropagation. Where does the supervision come from? Most data is unlabelled The vestibular-ocular reflex is an exception.

329 views • 14 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 8: Modeling text using a recurrent neural network trained with a really fancy optimizer. Geoffrey Hinton. The error surface for a linear neuron.

590 views • 18 slides

Geoffrey Hinton

CSC321: Introduction to Neural Networks and Machine Learning Lecture 23: Linear Support Vector Machines. Geoffrey Hinton. Getting good generalization on big datasets. If we have a big data set that needs a complicated model, the full Bayesian framework is very computationally expensive.

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The Age of Geoffrey Chaucer

The Age of Geoffrey Chaucer. 1340(CA)- 1400. Hundred Years’ War (1337-1453). War with France Based on possible ascension to French throne. With death of Charles IV of France, no direct male heirs existed. Dispute over the throne between: Philip VI of Valois (cousin of Charles IV)

357 views • 17 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 10: The Bayesian way to fit models. Geoffrey Hinton. The Bayesian framework. The Bayesian framework assumes that we always have a prior distribution for everything. The prior may be very vague.

349 views • 13 slides

Geoffrey Hinton

CSC321: Introduction to Neural Networks and Machine Learning Lecture 20 Learning features one layer at a time. Geoffrey Hinton. Learning multilayer networks. We want to learn models with multiple layers of non-linear features.

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The Outsiders S.E. Hinton

The Outsiders S.E. Hinton. Before we begin reading, we must get INTO the text…. Visualization. Think of the title of the novel The Outsiders . What sorts of images does the title bring to mind? Draw your visualizations in your notebook. Discussion.

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Geoffrey Hinton

CSC2535: Advanced Machine Learning Lecture 6a Convolutional neural networks for hand-written digit recognition. Geoffrey Hinton. Use many different copies of the same feature detector with different positions. Could also replicate across scale and orientation ( tricky and expensive)

731 views • 30 slides

Geoffrey Hinton

CSC321: Tutorial with extra material on: Semantic Hashing and Conditional Restricted Boltzmann Machines (This will not be in the final exam). Geoffrey Hinton. Finding binary codes for documents. 2000 reconstructed counts. Train an auto-encoder using 30 logistic units for the code layer.

546 views • 31 slides

Geoffrey Hinton University of Toronto

CSC321 Lecture 25: More on deep autoencoders & Using stacked, conditional RBM’s for modeling sequences. Geoffrey Hinton University of Toronto. Do the 30-D codes found by the autoencoder preserve the class structure of the data?.

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Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 11: Bayesian learning continued. Geoffrey Hinton. Bayes Theorem. conditional probability. joint probability. Probability of observed data given W. Prior probability of weight vector W.

288 views • 15 slides

Geoffrey Hinton

CSC321 Introduction to Neural Networks and Machine Learning Lecture 3: Learning in multi-layer networks. Geoffrey Hinton. Preprocessing the input vectors. Instead of trying to predict the answer directly from the raw inputs we could start by extracting a layer of “features”.

302 views • 16 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 9: Ways of speeding up the learning and preventing overfitting. Geoffrey Hinton. Five ways to speed up learning. Use an adaptive global learning rate Increase the rate slowly if its not diverging

359 views • 17 slides

Geoffrey Hinton

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 6: Applying backpropagation to shape recognition. Geoffrey Hinton. Applying backpropagation to shape recognition. People are very good at recognizing shapes It’s intrinsically difficult and computers are bad at it

321 views • 29 slides

Geoffrey Hinton

CSC2535: Computation in Neural Networks Lecture 11 Extracting coherent properties by maximizing mutual information across space or time. Geoffrey Hinton. The aims of unsupervised learning. We would like to extract a representation of the sensory input that is useful for later processing.

220 views • 19 slides

Geoffrey Hinton

CSC2535: Lecture 3: Ways to make backpropagation generalize better, and ways to do without a supervision signal. Geoffrey Hinton. Overfitting. The training data contains information about the regularities in the mapping from input to output. But it also contains noise

569 views • 54 slides

Geoffrey Hinton

CSC 2535 Winter 2006 Lecture 6a: Improving generalization by making the weights cheap to describe. Geoffrey Hinton. Sometimes it works better to use a weight penalty that has negligible effect on large weights. We can easily make up a heuristic cost function for this.

281 views • 24 slides

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