# Deep Learning

The choice to put deep learning in first place is based on the fact that all of us need it in experiment, and it's a practical and effortless way for a person without any background knowledge (those intended to read this tutorial section) to start devoting himself/herself in the lab.

The very first recommend one for deep learning is to watch Prof. Hung-Yi Lee's [online course ](https://www.youtube.com/playlist?list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49)which he generously puts on Youtube. Patiently watching all of it, and you will find yourself be able to complete any kind of tasks in the lab.

If you want to know more about Deep Learning in a mathematical way, you can checkout [Deep Learning](http://www.deeplearningbook.org/) by Ian Goodfellow, Yoshua Bengio and Aaron Courville.&#x20;



## Useful sources

1. [Annotated Seq2seq](https://bastings.github.io/annotated_encoder_decoder/?utm_campaign=NLP%20News\&utm_medium=email\&utm_source=Revue%20newsletter)
2. [Annotated transformer](http://nlp.seas.harvard.edu/2018/04/03/attention.html)
3. [Machine Learning: What are some tips and tricks for training deep neural networks?](https://www.quora.com/Machine-Learning-What-are-some-tips-and-tricks-for-training-deep-neural-networks)
4. &#x20;[Artificial Neural Networks: How can I estimate the number of neurons and layers?](https://www.quora.com/Artificial-Neural-Networks-How-can-I-estimate-the-number-of-neurons-and-layers)
