GitHub

Nice work by @hereismari getting started with @TensorFlow on Android!

  • If you want to make your own version of this app or want to know how to save your model and export it for Android or other devices check the very simple tutorial bellow.
  • A full example can be seen here

    Keep an in memory copy of eveything your model learned (like biases and weights) Example: , where w was learned from training.

  • Rewrite your model changing the variables for constants with value = in memory copy of learned variables.
  • Example: Also make sure to put names in the input and output of the model, this will be needed for the model later.
  • Example:

    Export your model with:

    tf.train.write_graph(, , .

mnist-android-tensorflow – Handwritten digits classification from MNIST with TensorFlow in Android; Featuring Tutorial!
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Dropout with Theano – Rishabh Shukla

Dropout with Theano  #DeepLearning #NeuralNetworks

  • In very simple terms – Dropout is a highly efficient regularization technique, wherein, for each iteration we randomly remove some of the neurons in a DNN(along with their connections; have a look at Fig. 1).
  • Here is our main Dropout function with three arguments: – A RandomStream generator, – Any theano tensor(Weights of a Neural Net), and – a float value to denote the proportion of neurons to drop.
  • So, while the model is in training phase, we’ll use dropout for our model weights and in test phase, we would simply scale the weights to compensate for all the training steps, where we omitted some random neurons.
  • Starting from the first line, we are creating a theano tensor variable , for input(words) and another variable of type , which will take a float value to denote the proportion of neurons to be dropped.
  • A few more methods, that are increasingly being used in DNNs now a days(I am omitting the standard L1/L2 regularization here):

    The reason I wanted to write about this, is because if you are working with a low level library like Theano, then sometimes using modules like might get a bit tricky.

Implementing a Dropout Layer with Numpy and Theano along with all the caveats and tweaks.
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Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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Can you tell if this music was composed by artificial intelligence?

Can you tell if this music was composed by artificial intelligence?  #technology

  • Davos download: global economic warming
  • The results showed that more than half the listeners attributed DeepBach-generated harmonies to Bach, while music by Bach was correctly identified by 75 percent of the listeners. “
  • The article is published in collaboration with Futurism .
  • Music is mathematical, and composers like Bach often made music that followed a defined, step-like flow that is almost algorithmic.
  • The views expressed in this article are those of the author alone and not the World Economic Forum.

Baroque composer Johann Sebastian Bach is known to have written many chorale cantatas, polyphonic hymns based on Lutheran texts. Each is fairly simple, featuring a single melody accompanied by three harmonies, so Gaetan Hadjeres and Francois Pachet from Sony Computer Science Laboratories in Paris thought it would be interesting to see if a machine could create chorale cantatas indistinguishable from Bach’s.
Continue reading “Can you tell if this music was composed by artificial intelligence?”

Scikit-Learn Cheat Sheet: Python Machine Learning

Cheat sheet: #machinelearning in #Python with scikit-learn -

  • The cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • Begin with our scikit-learn tutorial for beginners , in which you’ll learn in an easy, step-by-step way how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values.
  • If you still have no idea about how scikit-learn works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.
  • The scikit-learn cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • If you’re still quite new to the field, you should be aware that machine learning, and also this Python library, belong to the must-knows for every aspiring data scientist.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
Continue reading “Scikit-Learn Cheat Sheet: Python Machine Learning”

Can you tell if this music was composed by artificial intelligence?

Can you tell if this music was composed by artificial intelligence?  #technology

  • The results showed that more than half the listeners attributed DeepBach-generated harmonies to Bach, while music by Bach was correctly identified by 75 percent of the listeners. “
  • The article is published in collaboration with Futurism .
  • Stop overestimating the threat posed by Russia’s ‘new’ form of warfare
  • Music is mathematical, and composers like Bach often made music that followed a defined, step-like flow that is almost algorithmic.
  • Music isn’t the only field where AI algorithms have performed considerably well, even outperforming actual people in some cases.

Baroque composer Johann Sebastian Bach is known to have written many chorale cantatas, polyphonic hymns based on Lutheran texts. Each is fairly simple, featuring a single melody accompanied by three harmonies, so Gaetan Hadjeres and Francois Pachet from Sony Computer Science Laboratories in Paris thought it would be interesting to see if a machine could create chorale cantatas indistinguishable from Bach’s.
Continue reading “Can you tell if this music was composed by artificial intelligence?”

Human or AI: Can You Tell Who Composed This Music?

Human or #AI: can you tell who composed this music?

  • The results showed that more than half the listeners attributed DeepBach-generated harmonies to Bach, while music by Bach was correctly identified by 75 percent of the listeners. “
  • Music isn’t the only field where AI algorithms have performed considerably well, even outperforming actual people in some cases.
  • Interestingly enough, Bach’s music makes it into “Westworld,” a TV series that exploits the topic of AI.
  • Baroque composer Johann Sebastian Bach is known to have written many chorale cantatas, polyphonic hymns based on Lutheran texts.
  • Music is mathematical, and composers like Bach often made music that followed a defined, step-like flow that is almost algorithmic.

More than 50% of listeners were convinced that the harmonies created by neural network DeepBach were actually written by famous composer Johann Sebastian Bach. DeepBach is one of many examples of an AI system performing as well as or even better than its human counterparts, a sign that the technology is significantly improving.
Continue reading “Human or AI: Can You Tell Who Composed This Music?”