Update Time: 25/06/2018: I have added a new tutorial for deep learning, please go to my Github and see the notebooks on learning Python and TensorFlow, here is my GitHub repository: Data_Science_Python.
The following is the old post:
I’m sharing a lecture note of ” Deep Learning Tutorial – From Perceptrons to Deep Networks “. This lecture note is mainly based on the blog of Mr. Ivan Vasilev (A Deep Learning Tutorial: From Perceptrons to Deep Networks) with combinations of other materials collected and summarised from websites, and research papers. The contents are rephrased and modified to make it more readable (easy :P).
However, this note only provides a general view of Deep Learning, a lot of details or programming tools have been omitted to shorten the slides. Learners could use this as a shortcut to get familiar with all the basic deep learning concepts. If anyone wants the solid Tutorial of DL, I hope my previous post ( Open Courses, Free Softwares and Frameworks of Deep Learning ) could help.
This draft was made in my spare time voluntarily and the only benefit for me is the joy of sharing good knowledge with whoever like deep learning and neural networks. Personally, I am still learning DL and trying to put more information in the draft. If you find any mistake or confusing part in this version, please leave comments, I appreciate all the help from you.
PS: If there is any violation of the IP right, please feel free to contact me.
PPS: I updated the PPT version on 28/02/2017. Check the link in the end.
Here is a Quick Overview of the Slides：
You could download the slides in PDF format: lecture-note-a-deep-learning-tutorial-v2;
And PPT format for your own use: lecture-note-a-deep-learning-tutorial-v2;
The original PowerPoint format will be released in the future when modifications and updates are finished ( I hope 😛 ).
All documents follow the creative commons (CC) licenses .