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Deep Learning Study Material

DLSM 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# DLSM
**Deep Learning Study Material**

Here you can find deep learning material that are related to computer vision and medical imaging analysis.

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## Convolutional Neural Networks Basics

[Stanford course](http://cs231n.stanford.edu/syllabus.html)

### 01
* Intro to Computer Vision, historical context [[slides]](http://cs231n.stanford.edu/slides/winter1516_lecture1.pdf) [[video]](https://youtu.be/2uiulzZxmGg?list=PLrZmhn8sSgye6ijhLzIIXiU9GNaIwbF8B)
* Image classification, k-nearest neighbor, and linear classification [[slides]](http://cs231n.stanford.edu/slides/winter1516_lecture2.pdf) [[video]](https://youtu.be/ngXbD21b4qk?list=PLrZmhn8sSgye6ijhLzIIXiU9GNaIwbF8B)
	* Image classification [[notes]](http://cs231n.github.io/classification/)
	* Linear classification [[notes]](http://cs231n.github.io/linear-classify/)

### 02
* Optimization, stochastic gradient descent [[notes]](http://cs231n.github.io/optimization-1) [[video]](https://youtu.be/i1gGsE66b5s?list=PLrZmhn8sSgye6ijhLzIIXiU9GNaIwbF8B)

### 03
* Python/numpy tutorial [[link]](http://cs231n.github.io/python-numpy-tutorial/)
* Assignment #1 (kNN/SVM/Softmax) [[link]](http://cs231n.github.io/assignments2016/assignment1/)
* [Extra] Style Guide for Python Code [[link]](https://www.python.org/dev/peps/pep-0008/)
* [Extra] Kevin Keraundren's Tutorial for Python and Medical Image Analysis [[link]](https://github.com/curiale/Medical-Image-Analysis-IPython-Tutorials)

### 04
* Backpropagation and Introduction to neural networks [[notes]](http://cs231n.github.io/optimization-2/) [[video]](https://www.youtube.com/watch?v=59Hbtz7XgjM&list=PLrZmhn8sSgye6ijhLzIIXiU9GNaIwbF8B&index=4)
* Neural Networks Part 1 [[notes]](http://cs231n.github.io/neural-networks-1/)
* [Extra] Backpropagation MIT lecture [[video]](https://www.youtube.com/watch?v=q0pm3BrIUFo)

### 05
* Neural network part-II  -- setting up the data and the model [[notes]](http://cs231n.github.io/neural-networks-2/) [[video]](https://www.youtube.com/watch?v=KaR4lIdI1MQ&index=6&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA)
* Neural network part-III -- learning the network [[notes]](http://cs231n.github.io/neural-networks-3/) [[video]](https://www.youtube.com/watch?v=KaR4lIdI1MQ&index=6&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA)

### 06
* Assignment #1 (2-Layer Net) [[link]](https://github.com/amiralansary/DLSG/blob/master/code/stanford_course_cs231n/assignment1/two_layer_net.ipynb)
* Neural network case study [[link]](http://cs231n.github.io/neural-networks-case-study/)

### 07
* Assignment #2 (Fully-connected Neural Network/Batch Normalization/Dropout) [[link]](http://cs231n.github.io/assignments2016/assignment2/)
* [Extra] Batch normalization backpropagation [[link1]](https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html) [[link2]](http://cthorey.github.io./backpropagation/)

### 08
* Convolutional Neural Networks: architectures, convolution / pooling layers [[notes]](http://cs231n.github.io/convolutional-networks/) [[video]](https://www.youtube.com/watch?v=V8JDMkARdfU&index=7&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA)
* Assignment #2 (ConvNet on CIFAR-10) [[link]](http://cs231n.github.io/assignments2016/assignment2/)

---

## Reinforcement Learning
* UCL DeepMind course [[link]](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
* "Reinforcement Learning:An Introduction" Sutton et al. [[1st edition]](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-1st.html) [[2nd edition]](https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)
* "Algorithms for Reinforcement Learning" Szepesv´ari [[book]](https://sites.ualberta.ca/~szepesva/RLBook.html)
* Simple article about rl [[link]](http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/)

---

## Tensorflow Tutorials
* tensorflow [[ppt]](https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf)
* tensorflow for begginers [[link1]](https://www.tensorflow.org/versions/r0.11/tutorials/mnist/beginners/index.html) [[link2]](https://www.tensorflow.org/versions/r0.11/tutorials/mnist/pros/index.html)
* tensorflow examples and notebook [[link1]](https://github.com/aymericdamien/TensorFlow-Examples) [[link2]](https://github.com/nlintz/TensorFlow-Tutorials)
* tensorflow presentation [[link]](https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf)
* dive-into-tensorflow-part-v-deep-mnist [[link]](http://textminingonline.com/dive-into-tensorflow-part-v-deep-mnist)
* Reinforcement Learning Tutorial [[link]](http://outlace.com/Reinforcement-Learning-Part-1/)

---

## Useful Links for Deep Learning

### Important Links
+ [Awesome deep vision](https://github.com/kjw0612/awesome-deep-vision#understanding-cnn)
+ [Awesome computer vision](https://github.com/jbhuang0604/awesome-computer-vision)
+ [Awesome machine learning](https://github.com/josephmisiti/awesome-machine-learning)
+ [Deeplearning.net](http://deeplearning.net/)
+ [Deep learning nature article](http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html)
+ [CVIT summer school resources](http://cvit.iiit.ac.in/summerschool/resources.html)

### Libraries
+ [Software links](http://deeplearning.net/software_links/)
+ [Pyimagesearch](http://www.pyimagesearch.com/2016/06/27/my-top-9-favorite-python-deep-learning-libraries/)

### Tutorials
+ [Theano](http://deeplearning.net/software/theano/tutorial/)
+ [Lasagne](http://lasagne.readthedocs.io/en/latest/user/tutorial.html)
+ [Caffe](http://caffe.berkeleyvision.org/tutorial/)
+ [Tensorflow](https://www.tensorflow.org/versions/r0.9/tutorials/index.html)
+ [Keras](https://github.com/jocicmarko/ultrasound-nerve-segmentation) Deep Learning Tutorial for Kaggle Ultrasound Nerve Segmentation competition, using Keras

### Courses
+ [Stanford](http://cs231n.stanford.edu/syllabus.html)
+ [Hinton](http://www.cs.toronto.edu/~hinton/csc2535/index.html)
+ [Nando de Freitas](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
+ [Hugo Larochelle](http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html)
+ [Udacity Deep Learning Course](https://www.udacity.com/course/deep-learning--ud730)
+ [Bargava from Cisco systems](https://github.com/rouseguy/scipyUS2016_dl-image)

### Videos
+ [Deep Learning Summer School (DLSS), Montreal 2016](http://videolectures.net/deeplearning2016_montreal/)
+ [Hugo Larochelle's Youtube](https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH)
+ [Nando de Freitas's Youtube](https://www.youtube.com/playlist?list=PLjK8ddCbDMphIMSXn-w1IjyYpHU3DaUYw)
+ [CS231 Winter 2016 All Youtube](https://www.youtube.com/playlist?list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA)

### Textbooks
+ [Ian Goodfellow, Yoshua Bengio and Aaron Courville textbook](http://www.deeplearningbook.org/)
+ [Microsoft textbook](http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf)
+ [Michael Nielsen's online textbook](http://neuralnetworksanddeeplearning.com/)

### Blogs
+ [Hacker News for Data Science](http://www.datatau.com/news)
+ [i am trusk](http://iamtrask.github.io/)
+ [Colin Morris](https://colinmorris.github.io/)

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