https://www.blog.google/topics/journalism-news/how-publishers-can-take-advantage-machine-learning/

]]>Links on work in this area including open source code.

- Convert a document or paragraph into a vector representation Doc2Vec https://arxiv.org/pdf/1405.4053.pdf
- Using lstm/gru to represent sentences, works better than Doc2Vec for information retrieval tasks. Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval https://arxiv.org/pdf/1502.06922.pdf

- Survey of Deep Recommendation Engines. Good starting point https://arxiv.org/pdf/1707.07435.pdf

- Google Deep and Wide https://arxiv.org/pdf/1606.07792.pdf
- Multitask Recommender System Using GRU https://arxiv.org/pdf/1609.02116.pdf
- DeepFM, no need for feature engineering as in Google Deep and Wide https://arxiv.org/abs/1703.04247
- Multi-Rate Deep Learning for Temporal Recommendation. Using multiple time scales and user features trains using DSSM (Deep Semantic Structured Model). http://sonyis.me/paperpdf/spr209-song_sigir16.pdf
- YouTube Recommendation System https://pdfs.semanticscholar.org/bcdb/4da4a05f0e7bc17d1600f3a91a338cd7ffd3.pdf
- Session Based Recommendation System https://arxiv.org/pdf/1511.06939.pdf . Only uses sequence of content, and not the content itself.

- Subreddit recommendation. RNN based, does not use content. https://cole-maclean.github.io/blog/RNN-Based-Subreddit-Recommender-System

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https://github.com/tensorflow/tensorflow/tree/r1.2/tensorflow/contrib/ios_examples

Just ran first ran deep learning model with the camera app example. Pretty good image recognition!!

The next level is object detection, i.e creating a bounding box around detected image.

]]>- Efficient Backprop. Paper on back propagation, the sauce of neural networks by Yann Lecun from AT&T l in 98 – http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf
- Gradient Base Learning Applied Document Recognition. Paper described how to do recognition of hand written characters. Also describes LeNet-5 (the original convolution neural network) by Yann Lecun. https://pdfs.semanticscholar.org/d3f5/87797f95e1864c54b80bc98e957da6746e27.pdf

- DropOut – https://arxiv.org/abs/1207.0580
- Batch Normalization – https://arxiv.org/abs/1502.03167

These are the recent advances for CNN, original was Lecun-5 in the 98 paper mentioned above .

- AlexNet brought back neural network revolution by winning ImageNet competition- https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
- VGG Net a very deep NN – https://arxiv.org/abs/1409.1556.pdf
- GoogleNet – https://arxiv.org/abs/1409.4842
- Residual Neural Network – The deepest Neural network 152 layers – https://arxiv.org/abs/1512.03385

Finding a bounding box around different objects is harder than simply classifying an image. This a class of image localization and detection problems.

- Faster RCNN (there is also Fast RCNN and RCNN, Faster is incremental improvement over all), one of the best – https://arxiv.org/abs/1506.01497
- YOLO, supposed to be the most efficient – https://arxiv.org/abs/1612.08242

One of the hottest areas of research. This is a class of algorithms where 2 neural networks collaborate to generate e.g. realistic images. One network produces fake images (faker), and the other network learns to decipher fake from real (detective). Both networks compete with each other and try to be good at their jobs, till the faker is so good that it can generate realistic images. Fake it till you make it!

- Generative Adversarial Neural Networks – https://arxiv.org/abs/1406.2661
- CycleGAN – Change doodles to real images https://arxiv.org/abs/1703.10593
- Conditional GAN – Control the output of GAN by classes https://arxiv.org/abs/1411.1784

Getting labeled data is expensive, while unlabeled data is abundant. Techniques to use little bit of training data and lots of unlabeled data.

- Stacked What Where Auto encoders – https://arxiv.org/abs/1506.02351
- Ladder Networks – https://arxiv.org/abs/1507.02672
- Pseudo Labels – http://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf
- Surrogate Class – http://papers.nips.cc/paper/5548-discriminative-unsupervised-feature-learning-with-convolutional-neural-networks.pdf

Research on being able to ask question on images. e.g. asking if there are there more blue balls than yellow about an image.

- Inferring and Executing Programs For Visual Reasoning – https://arxiv.org/abs/1705.03633
- Relation Networks From Deep Mind, generic NN component that can be used on visual and text QA systems – https://arxiv.org/abs/1706.01427.pdf

Being able to take a picture and a style image e.g. a painting, and redraw the picture in the painting style. See my blog on painting like Picaso.

- Neural Artistic Style – https://arxiv.org/abs/1508.06576

- LSTM – http://dl.acm.org/citation.cfm?id=1246450 Blog explaining LSTM – http://colah.github.io/posts/2015-08-Understanding-LSTMs/
- GRU – https://arxiv.org/abs/1502.02367

This is area of unsupervised learning. An auto encoder is a neural network that tries to recreate the original image. e.g. give it any picture and it will try to recreate the same image. Why would anyone want to do that. The neural network tries to learn a condensed representation of images given that there are commonalities. Auto encoders can be used to pre train a neural network with unlabeled data.

- Lecture Notes Sparse Auto encoders – https://web.stanford.edu/class/cs294a/sparseAutoencoder.pdf

- Reading Text in the Wild – https://www.robots.ox.ac.uk/~vgg/publications/2016/Jaderberg16/jaderberg16.pdf

- Neural Programmer Interpreters, learn to program for simple tasks – https://arxiv.org/abs/1511.06279

- Visual Interaction Networks – Deep mind paper to learn to predict physical future of objects from a few frames – https://arxiv.org/abs/1706.01433

https://github.com/navacron/deeplearning/tree/master/pytorch/catgan

The following is output conditioned on golden cats. By favorite one is 3rd one from the right in the first row. Everytime the GAN is run it will generate unique cats like these. For more cats visit the github page.

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https://github.com/navacron/deeplearning/tree/master/pytorch

This uses the autograd feature that is unique to pytoch and torch (not available in tensorflow). This is pytorch version of cs231n http://cs231n.github.io/neural-networks-case-study/

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Free online courses

- Deep Learning Convolution Neural Networks, Stanford, http://cs231n.stanford.edu/
- Deep Learning Natural Language Processing, Stanford http://cs224d.stanford.edu/syllabus.html
- Deep Learning Udacity, https://www.udacity.com/course/deep-learning–ud730

Paid courses

- Deep Learning, NYU, http://cilvr.cs.nyu.edu/doku.php?id=deeplearning:slides:start
- Deep Learning Natural Language Processing, Stanford Online, http://scpd.stanford.edu/search/publicCourseSearchDetails.do?method=load&courseId=11754
- Self Driving Car Engineer, Udacity, https://www.udacity.com/drive
- Introduction to Deep Learning, Princeton, https://www.cs.princeton.edu/courses/archive/spring16/cos495/

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