We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.

Backpropagation part 2, deep learning optimization algorithms, tips and tricks and project advice.

В четверг 25-го октября состоялось четвёртое занятие по курсу Deep Learning in Natural Language Processing.
На семинаре мы обсудили:

Lecture 13 provides a mini tutorial on Azure and GPUs followed by research highlight "Character-Aware Neural Language Models." Also covered are CNN Variant 1 and 2 as well as comparison between sentence models: BoV, RNNs, CNNs.

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Natural Language Processing with Deep Learning

Instructors:
- Chris Manning
- Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks,…

Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 6.

Get in touch on Twitter @cs231n, or on Reddit /r/cs231n.

Вопросы можно задавать в телеграме

Lecture 8 covers traditional language models, RNNs, and RNN language models. Also reviewed are important training problems and tricks, RNNs for other sequence tasks, and bidirectional and deep RNNs.

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Natural Language Processing with Deep Learning

Instructors:
- Chris Manning
- Richard Socher

Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks…

Musings of a Computer Scientist.