- Deeplearning
- Data Science
- Machine Learning
- Statistical Modelling
- Unsupervised Learning
- Reinforcement Learning
- Image Processing
- Fundamental Deep Learning code in TFLearn, Keras, Theano and TensorFlow: https://insights.untapt.com/fundamental-deep-learning-code-in-tflearn-keras-theano-and-tensorflow-66be10a03227#.k2jw09ft2
- http://white.ucc.asn.au/2017/01/24/JuliaML-and-TensorFlow-Tuitorial.html
- https://github.com/oxford-cs-deepnlp-2017/lectures
- The key to building a data science portfolio : https://www.dataquest.io/blog/build-a-data-science-portfolio/
- http://www.datasciencecentral.com/profiles/blogs/66-job-interview-questions-for-data-scientists
- How to Tell a Compelling Story with Data - 6 Rules & 6 Tools.
- Tidy Data in Python
- David Venturi : I Dropped Out of School to Create My Own Data Science Master’s — Here’s My Curriculum.
- [Golang libraries for data science](http://www.mjhall.org/golang-data-science-libraries/(
- Chunking, https://en.wikipedia.org/wiki/Chunking_%28psychology%29
- De-Chunking your brain to stop Companies Learning Your Secrets, http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html
- Shiva Bhaskar on Algorithms, Big Data And Accountability.
- With new algorithms, data scientists could accomplish in days what once took month: http://phys.org/news/2016-10-algorithms-scientists-days-months.html
- https://www.opendatascience.com/blog/how-to-hire-machine-learning-consultants/
- Book, http://www.librarything.com/work/15575877
- http://www.datasciencemasters.org
- https://www.simplilearn.com/data-science-interview-questions-article
- JuliaML and TensorFlow Tutorial
- https://github.com/pytorch/pytorch
- 2017: Tran D., et al., Deep Probabilistic Programming, 13 Jan 2017, https://arxiv.org/abs/1701.03757v1
- Theis L., et al., A note on the evaluation of generative models, DOI:arXiv:1511.01844v3, https://arxiv.org/abs/1511.01844
- The 10 Algorithms Machine Learning Engineers Need to Know, https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.fcs1pf3la
- An Introduction to Machine Learning in Julia: http://juliacomputing.com/blog/2016/09/28/knn-char-recognition.html
- Java code: http://aispace.org/dTree/
- {Paper} : Evaluation of Deep Learning Frameworks.
- Setting up a Deep Learning Machine from Scratch for Kubuntu.
- Julia Libraries, https://github.com/svaksha/Julia.jl/blob/master/AI.md#machine-learning
- Python libraries, https://github.com/svaksha/pythonidae/blob/master/AI.md#machine-learning
- https://github.com/hangtwenty/dive-into-machine-learning
- https://www.udacity.com/podcasts/linear-digressions
- https://soundcloud.com/oreilly-radar/sets/the-oreilly-data-show-podcast (feed here)
- http://www.thetalkingmachines.com/ways-to-listen
- http://www.partiallyderivative.com/
- http://mathworld.wolfram.com/MaximumLikelihood.html
- Scikit-Learn CheatSheet: Python Machine Learning.
- http://gael-varoquaux.info/programming/mloss-2015-wising-up-to-building-open-source-machine-learning.html
- http://techcrunch.com/2016/04/02/how-to-approach-machine-learning-as-a-non-technical-person/
- https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software
- https://en.wikipedia.org/wiki/Stochastic_gradient_descent
- https://en.wikipedia.org/wiki/Long_short-term_memory
- http://colah.github.io/posts/2015-08-Understanding-LSTMs/
- http://deeplearning.net/tutorial/lstm.html
- https://scholar.google.de/scholar?q=compositional+knowledge+based+application&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwigqrC7qfDMAhWGXSwKHZ2bAQoQgQMIMjAA
- Building an efficient neural language model over a billion words: https://code.facebook.com/posts/1827693967466780/
- http://www.aclweb.org/anthology/P15-1016
- 2015, Grammar as a Foreign Language.
- https://github.com/tensorflow/models/tree/master/syntaxnet
- http://swami.wustl.edu/epoxidation-paper
- Mitchell: http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf
- Tom Mitchell book on ML: http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html
- Deep Learning : http://www.deeplearningbook.org/ Information Science and Statistics
- Bishop: Pattern Recognition and Machine Learning, Springer.
- Akaike and Kitagawa: The Practice of Time Series Analysis.
- Cowell, Dawid, Lauritzen, and Spiegelhalter: Probabilistic Networks and Expert Systems.
- Doucet, de Freitas, and Gordon: Sequential Monte Carlo Methods in Practice.
- Fine: Feedforward Neural Network Methodology.
- Hawkins and Olwell: Cumulative Sum Charts and Charting for Quality Improvement.
- Jensen: Bayesian Networks and Decision Graphs.
- Marchette: Computer Intrusion Detection and Network Monitoring:A Statistical Viewpoint.
- Murphy, "Machine Learning"
- Rubinstein and Kroese: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte Carlo Simulation, and Machine Learning.
- Studený: Probabilistic Conditional Independence Structures.
- Vapnik: The Nature of Statistical Learning Theory, Second Edition.
- Wallace: Statistical and Inductive Inference by Minimum Massage Length.
- Many Regression Algorithms, One Unifed Model
- http://www.freekstulp.net/publications/
- https://en.wikipedia.org/wiki/Linear_regression
- https://en.wikipedia.org/wiki/Regression_analysis
- https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation
- http://mathworld.wolfram.com/MaximumLikelihood.html
- https://en.wikipedia.org/wiki/Monty_Hall_problem
Cramer Rao Bounds
- http://willett.ece.wisc.edu/wp-uploads/2016/01/16-CRLB.pdf
- CHAPTER 2. Cramer-Rao lower bound: http//www.cs.tut.fi/~hehu/SSP/lecture2.pdf
- https://en.wikipedia.org/wiki/Cram%C3%A9r%E2%80%93Rao_bound
- https://en.wikipedia.org/wiki/Linde%E2%80%93Buzo%E2%80%93Gray_algorithm
- http://www.data-compression.com/vq.shtml#animation
- http://www.data-compression.com/vqanim.shtml
- http://karpathy.github.io/2016/05/31/rl/
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto
- Barbara Zitova, Jan Flusser: Image registration methods: a survey