Explore >> Select a destination
|
You are here 
|
swethatanamala.github.io | ||
| | | | |
jalammar.github.io
|
|
| | | | | Translations: Chinese (Simplified), French, Japanese, Korean, Persian, Russian, Turkish, Uzbek Watch: MIT's Deep Learning State of the Art lecture referencing this post May 25th update: New graphics (RNN animation, word embedding graph), color coding, elaborated on the final attention example. Note: The animations below are videos. Touch or hover on them (if you're using a mouse) to get play controls so you can pause if needed. Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016. These models are explained in the two pioneering papers (Sutskever et al., 2014, Cho et al., 2014)... | |
| | | | |
research.google
|
|
| | | | | Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are n... | |
| | | | |
www.v7labs.com
|
|
| | | | | Recurrent neural networks (RNNs) are well-suited for processing sequences of data. Explore different types of RNNs and how they work. | |
| | | | |
sirupsen.com
|
|
| | | [AI summary] The article provides an in-depth explanation of how to build a neural network from scratch, focusing on the implementation of a simple average function and the introduction of activation functions for non-linear tasks. It discusses the use of matrix operations, the importance of GPUs for acceleration, and the role of activation functions like ReLU. The author also outlines next steps for further exploration, such as expanding the model, adding layers, and training on datasets like MNIST. | ||
