VirtualTam's bookmarks

101. Deep Learning From Scratch: Theory and Implementation – deep ideas 2017-09-06

     tags: deep-learning, machine-learning, machine-learning/tensorflow, neural-network, programming/python

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     • http://www.deepideas.net/deep-learning-from-scratch-i-computational-graphs/
     • http://www.deepideas.net/deep-learning-from-scratch-ii-perceptrons/
     • http://www.deepideas.net/deep-learning-from-scratch-iii-training-criterion/
     • http://www.deepideas.net/deep-learning-from-scratch-iv-gradient-descent-and-backpropagation/
     • http://www.deepideas.net/deep-learning-from-scratch-v-multi-layer-perceptrons/
102. model selection - How to choose the number of hidden layers and nodes in a feedforward neural network? - Cross Validated 2017-08-22

     tags: hidden, machine-learning, neural-network

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103. What is back propagation in neural networks? 2017-08-22

     tags: machine-learning, neural-network, recurrent, synapse

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     • https://en.wikipedia.org/wiki/Backpropagation
     • https://www.quora.com/How-do-you-explain-back-propagation-algorithm-to-a-beginner-in-neural-network
     • https://stackoverflow.com/questions/38340791/guided-back-propagation-in-tensorflow#38341080
     • https://www.tensorflow.org/tutorials/recurrent
     • https://stackoverflow.com/questions/40868896/backpropagation-through-time-bptt-of-lstm
104. machine learning - What does the hidden layer in a neural network compute? - Cross Validated 2017-08-20

     tags: hidden, layer, machine-learning, neural-network, synapse

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     • https://static.googleusercontent.com/media/research.google.com/en//archive/unsupervised_icml2012.pdf
     • https://www.cs.cmu.edu/~dst/pubs/byte-hiddenlayer-1989.pdf
105. Python | General knowledge & job interview questions 2017-08-20

     tags: exercise, knowledge, programming/job-interview, programming/python

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     • https://www.quora.com/What-are-good-Python-interview-questions
     • https://www.reddit.com/r/Python/comments/1knw7z/python_interview_questions/
     • https://resources.workable.com/python-developer-interview-questions
       • https://www.reddit.com/r/Python/comments/dxvtv/favorite_python_interview_question/
       • https://softwareengineering.stackexchange.com/questions/21917/python-interview-questions
       • http://www.ilian.io/python-interview-question-and-answers/
     • https://stackoverflow.com/questions/2573135/python-progression-path-from-apprentice-to-guru/2576240#2576240
     • https://docs.python.org/3/tutorial/
     • https://www.reddit.com/r/Python/comments/6v0amj/the_more_i_learn_about_python_the_more_i_realized/
     • https://github.com/00111000/Imports-in-Python

     Language:

     • GIL
     • memory management, object references
     • data structures
     • duck typing
     • monkey patching
     • generators
     • list & dict comprehensions
     • decorators
     • introspection
     • ...

     Development tools:

     • debugging
     • packaging
     • lint
     • testing: unitary, functional, TDD, BDD

     Libraries:

     • data parsing
     • data scraping
     • database management
     • http requests
     • web frameworks
     • ...
106. TensorFlow for Poets « Pete Warden's blog 2017-08-20

     tags: data, deep-learning, machine-learning, machine-learning/tensorflow, mobile/android, programming/java, programming/python, visualization

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     • https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/
     • https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2/
     • https://www.tensorflow.org/serving/
107. #patchaday | Anton Woldhek 2017-08-17

     tags: audio, filter, multimedia/pure-data, patch, synthesis, visualization

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     • http://designingsound.org/2014/10/patch-a-day-challenge/
     • https://github.com/rjdj/rjlib
     • https://forum.pdpatchrepo.info/topic/8730/a-lava-lamp
     • http://sepulchra.com/blog/how-i-learned-to-stop-worrying-and-love-ms/
108. SVMs - An overview of Support Vector Machines - SVM Tutorial 2017-08-17

     tags: data, machine-learning, support-vector-machine

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     • https://www.svm-tutorial.com/2014/11/svm-understanding-math-part-1/
     • https://www.svm-tutorial.com/2014/11/svm-understanding-math-part-2/
109. Michal Kosinski: The End of Privacy | Stanford Graduate School of Business 2017-05-29

     tags: data, machine-learning, privacy, security

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     • https://www.gsb.stanford.edu/insights/michal-kosinski-ai-already-smarter-we-are
     • https://www.gsb.stanford.edu/insights/michal-kosinski-can-we-out-evolve-artificial-intelligence
110. Dvlv/Tkinter-By-Example: Learn Tkinter By Example - a free book 2017-05-28

     tags: programming/python, tk, user-interface

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     • https://www.reddit.com/r/Python/comments/6dnfsw/my_first_book_tkinter_by_example_now_available/
111. Natural Language Processing (Almost) from Scratch 2017-05-24

     tags: machine-learning, natural-language-processing, research

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112. dlib C++ Library 2017-05-24

     tags: deep-learning, machine-learning, natural-language-processing, programming/c++, programming/python

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     • https://github.com/davisking/dlib

     via http://blog.dlib.net/2014/04/mitie-completely-free-and-state-of-art.html via https://www.reddit.com/r/MachineLearning/comments/26pzim/current_state_of_the_art_in_named_entity/

113. How to stop hopping the learning chain and actually begin somewhere - Academia Stack Exchange 2017-05-10

     tags: art, development, paper, research, state, training, workflow

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     • https://academia.stackexchange.com/questions/39022/how-not-to-be-like-a-rolling-stone-in-research
114. gplearn - Genetic Programming in Python, with a scikit-learn inspired and compatible API 2017-05-05

     tags: data-science, genetic-algorithm, programming, programming/python

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     • https://github.com/trevorstephens/gplearn
115. Distributed Evolutionary Algorithms in Python 2017-05-05

     tags: genetic-algorithm, machine-learning, programming, programming/python

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     • https://github.com/deap/deap
116. Introduction to Local Interpretable Model-Agnostic Explanations (LIME) - O'Reilly Media 2017-05-05

     tags: data, machine-learning, model

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     • http://www.kdnuggets.com/2016/08/introduction-local-interpretable-model-agnostic-explanations-lime.html
     • https://github.com/marcotcr/lime
     • https://www.youtube.com/watch?v=hUnRCxnydCc
117. Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano 2017-05-05

     tags: bayesian, data, machine-learning, markov, model, programming/python, stochastic

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     • https://github.com/pymc-devs/pymc3
     • https://github.com/pymc-devs/pymc
     • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3097064/
118. Publicly Available Spam Filter Training Sets 2017-05-05

     tags: bayesian-filter, dataset, email, email/spam, machine-learning, spamassassin

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     • http://www.paulgraham.com/spamarchives.html
     • http://untroubled.org/spam/
     • https://spamassassin.apache.org/old/publiccorpus/
     • https://github.com/dmitrynogin/SpamAssassin
     • http://archive.ics.uci.edu/ml/datasets/Spambase + http://statweb.stanford.edu/~tibs/ElemStatLearn/
     • https://inclass.kaggle.com/c/adcg-ss14-challenge-02-spam-mails-detection
     • https://www.kaggle.com/rtatman/fraudulent-email-corpus
     • https://www.kaggle.com/veleon/ham-and-spam-dataset
     • https://www.kaggle.com/beatoa/spamassassin-public-corpus
     • http://www.csmining.org/index.php/spam-assassin-datasets.html
     • http://artinvoice.hu/spams/
     • http://www2.aueb.gr/users/ion/data/enron-spam/
     • https://security.stackexchange.com/questions/223731/spamassassin-public-corpus
119. Learn to Read the Source, Luke 2017-05-04

     tags: development, open-source, software, star-wars

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     • https://news.ycombinator.com/item?id=3769446
120. /r/ansible best practices that you've learned? 2017-04-26

     tags: automation, configuration-management/ansible, linux

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