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A list of paper, books and sites for various different topics related to machine learning and deep learning along with various field in which it is implemented

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# Basics of machine learning and deep learning and its application in various fields.

1. True understanding is making all parts of a concept so clear and vivid as if they are objects in front of you that you can manipulate as you wish.
2. This repository contains a list various tasks that can be achieved with Machine Learning/Deep Learning.
3. It doesn't contain any benchmark paper because I love in doing new things rather than improving previous research.

*Pull Requests are welcome to add any new blog, paper, site link.*

## Theory:
[Scikit Learn](http://scikit-learn.org/stable/) is a wonderful library for implementing basic machine learning tasks.

##### Naive Bayes:
* Basics of naive bayes with term frequency and inverse document frequecy intuition [[site](http://sebastianraschka.com/Articles/2014_naive_bayes_1.html)]

### Deep Learning:
* A beginners guide to understand neural nets. Easy explanation [[book](http://neuralnetworksanddeeplearning.com)]
* Detailed explanation with math [[book](http://www.deeplearningbook.org)]
* Dropouts [[paper](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)]
* Practical approach and code in python. [[Theano](http://deeplearning.net/tutorial/)] [[Tensorflow](tensorflow.org/tutorials/)] [[keras](https://github.com/fchollet/keras/)]
* RNNs and LSTMs Intuition [[site](http://blog.echen.me/2017/05/30/exploring-lstms/)]
* GANs [[first-paper]](https://arxiv.org/pdf/1406.2661.pdf)

##### Text Embedding:
* Word2vec [[paper](http://web2.cs.columbia.edu/~blei/seminar/2016_discrete_data/readings/MikolovSutskeverChenCorradoDean2013.pdf)]
* Doc2vec [[paper](https://cs.stanford.edu/~quocle/paragraph_vector.pdf)]
* SkipThought Vectors [[paper](https://arxiv.org/abs/1506.06726)] [[implementation](https://github.com/ryankiros/skip-thoughts)]

## Applications:
__Keyword Spotting system / Wake-word detection__
* Small-footprint keyword spotting using deep neural networks [[paper](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42537.pdf)]
* Convolutional Neural Networks for Small-footprint Keyword Spotting [[paper](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43969.pdf)]
* A hidden Markov model based keyword recognition system [[paper](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=115555&tag=1)]
***
__Voice activity detection__
* A simple but effective real-time voice activity detection algorithm [[paper](http://www.eurasip.org/Proceedings/Eusipco/Eusipco2009/contents/papers/1569192958.pdf)]
* Recurrent neural networks for voice activity detection [[paper](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41186.pdf)]
***
__Speech recognition and acoustic modeling__
* Training LVCSR systems on thousands of hours of data [[paper](http://svr-www.eng.cam.ac.uk/~ky219/papers/evermann-icassp05.pdf)]
* Applying CNN on hybrid NN-HMM model for speech recognition [[paper](http://www.cs.toronto.edu/~asamir/papers/icassp12_cnn.pdf)]
* Convolutional Neural Networks for Speech Recognition [[paper](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/TASLP2339736-proof.pdf)]
* Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling [[paper](http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43905.pdf)]
* Understanding how deep beleif networks perform acoustic modelling [[paper](http://www.cs.toronto.edu/~asamir/papers/icassp12_dbn.pdf)]
***
__Activity Detection__
* Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life [[paper](http://ieeexplore.ieee.org/document/5934365/)]
* A Study on Human Activity Recognition Using Accelerometer Data
from Smartphones [[paper](http://www.sciencedirect.com/science/article/pii/S1877050914008643)]
* A Practical Approach to Recognizing Physical Activities [[paper](https://www.cs.cornell.edu/~tanzeem/pubs/JonathanLester_EDAS-1568973904.pdf)]
***
__Face-recognition using FPGA__
* A self-configurable systolic architecture for face recognition system based on principal component neural network [[paper](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5739514)]
***
__Text Classification__
* Support Vector Machines and Word2vec for Text Classification with Semantic Features [[paper](http://ieeexplore.ieee.org/document/7259377/)]
***
__Time Series Analysis__
* An Introductory Study on Time Series Modeling and Forecasting [[paper](https://arxiv.org/abs/1302.6613)]
***
__Retinal vessel segmentation__
* Retinal Vessel Segmentation Using Deep Neural Networks [[paper](https://bib.irb.hr/datoteka/760800.clanak_review.pdf)]
* Segmenting Retinal Blood Vessels With Deep Neural Networks [[paper](http://ieeexplore.ieee.org/document/7440871/)] [[implementation](https://github.com/jain-anshul/segmenting-retina-unet)] 
* Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification[[paper](http://ieeexplore.ieee.org/document/1677727)]
***
__Text to Image Generation__
* Text to image Synthesis using GANs [[paper](https://arxiv.org/abs/1605.05396)] [[implementation](https://github.com/paarthneekhara/text-to-image)]
* StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [[paper](https://arxiv.org/abs/1612.03242)] [[implementation](https://github.com/hanzhanggit/StackGAN)]
* StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks [[paper](https://arxiv.org/abs/1710.10916)] [[implementation](https://github.com/hanzhanggit/StackGAN-v2)]
* AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks [[paper](https://arxiv.org/abs/1711.10485)]
***
__Object Classification and Recognition__
* YOLO9000: Better, Faster, Stronger [[paper](https://arxiv.org/abs/1612.08242)] [[implementation](https://pjreddie.com/darknet/yolo/)]
* Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [[paper](https://arxiv.org/abs/1506.01497)] [[implementation](https://github.com/ShaoqingRen/faster_rcnn)]

***
__Neural Talk 2__
* Image Captioning by Andrej Karpathy [[implementation](https://github.com/karpathy/neuraltalk2)]
***
__Neural VQA__
* Exploring Models and Data for Image Question Answering [[paper](https://arxiv.org/abs/1505.02074)] [[implementation](https://github.com/abhshkdz/neural-vqa)]
***
__Genre Classification__
* To classify songs based on genre automatically [[implementation](https://github.com/despoisj/DeepAudioClassification)]

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