Course "Theories of Deep Learning". Contribute to deepmipt/tdl development by creating an account on GitHub.

Всем привет! Сегодня мы детально обсудим очень важный класс моделей машинного обучения – линейных. Ключевое отличие нашей подачи материала от аналогичной в курс...

Stanford Winter Quarter 2016 class: CS231n: Convolutional Neural Networks for Visual Recognition. Lecture 5.

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

We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization.

Comments on Stanford cs224n lecture 4 https://youtu.be/uc2_iwVqrRI Main topics: Word embeddings retraining to a specific task Introduction to neural networks...

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…