PyTorch tutorial distilled – Towards Data Science – Medium

#PyTorch tutorial distilled - Moving from #TensorFlow to PyTorch  #Python

  • Sometimes it may be not very obvious why we should do this, but on the other hand, we have full control over our gradients, when and how we want to apply them.Static vs. dynamic computational graphsNext main difference between PyTorch and TensorFlow is their approach to the graph representation.
  • Note: in previous API forward/backward methods were not static and we stored required variables as self.save_for_backward(input) and access them as input, _ = self.saved_tensors.Train model with CUDAIf was discussed earlier how we might pass one tensor to the CUDA.
  • cpu() method.Also, PyTorch supports direct devices allocation at the source code:Because sometimes we want to run the same model on the CPU and the GPU without code modification I propose some kind of wrapper:Weight initializationIn TensorFlow weights initialization mainly are made during tensor declaration.
  • If your model was initialized with OrderedDict or class-based model string representation will contain names of the layers.As per PyTorch documentation saving model with state_dict() method is more preferable.LoggingLogging of the training process is a pretty important part.
  • I propose to split your models and all wrappers on such building blocks:And here is some pseudo code for clarity:ConclusionI hope with this post you’ve understood main points of PyTorch:It can be used as drop-in replacement of NumpyIt’s really fast for prototypingIt’s easy to debug and use conditional flowsThere are…

In this post, I will cover some basic principles as some advanced stuff at the PyTorch.
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Data in, intelligence out: Machine learning pipelines demystified

How machine learning pipelines work: Data in, intelligence out #AI #ML #datascience

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
Continue reading “Data in, intelligence out: Machine learning pipelines demystified”

Data in, intelligence out: Machine learning pipelines demystified

Master how to construct a  #machinelearning pipeline

  • It’s tempting to think of machine learning as a magic black box.
  • If you’re in the business of deriving actionable insights from data through machine learning, it helps for the process not to be a black box.
  • The more you know what’s inside the box, the better you’ll understand every step of the process for how data can be transformed into predictions, and the more powerful your predictions can be.
  • There’s also a pipeline for data as it flows through machine learning solutions.
  • Mastering how that pipeline comes together is a powerful way to know machine learning itself from the inside out.

Data plus algorithms equals machine learning, but how does that all unfold? Let’s lift the lid on the way those pieces fit together, beginning to end
Continue reading “Data in, intelligence out: Machine learning pipelines demystified”

AI learns to write its own code by stealing from other programs

#AI has to resort to copying and pasting from StackOverflow too. #programming

  • Ultimately, the approach could allow non-coders to simply describe an idea for a program and let the system build it, says Marc Brockschmidt, one of DeepCoder’s creators at Microsoft Research in Cambridge, UK.
  • DeepCoder uses a technique called program synthesis: creating new programs by piecing together lines of code taken from existing software – just like a programmer might.
  • “It could allow non-coders to simply describe an idea for a program and let the system build it”

    One advantage of letting an AI loose in this way is that it can search more thoroughly and widely than a human coder, so could piece together source code in a way humans may not have thought of.

  • DeepCoder created working programs in fractions of a second, whereas older systems take minutes to trial many different combinations of lines of code before piecing together something that can do the job.
  • Brockschmidt says that future versions could make it very easy to build routine programs that scrape information from websites, or automatically categorise Facebook photos, for example, without human coders having to lift a finger

    “The potential for automation that this kind of technology offers could really signify an enormous [reduction] in the amount of effort it takes to develop code,” says Solar-Lezama.

Software called DeepCoder has solved simple programming challenges by piecing together bits of borrowed code
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The source code behind PhD APIs

Are you developing with the latest technology? Take a look at deep learning source codes:

  • Get sample code for implementing deep learning and natural language processing APIs
  • The code transforms a tedious manual content workflow into a more automated, cost-efficient process.
  • As the API economy enters its second decade, many organizations, from startups to Fortune 500, are recognizing the transformational power of APIs and creating game-changing applications.
  • Savvy developers know where the APIs are and how to put them to use in a way that enables iterative and agile application development.
  • We partnered with ProgrammableWeb to put together a use case to provide the source code and thinking behind NewsMedia Inc.’s (a fictitious company) implementation of deep learning and natural language processing APIs available on Watson Developer Cloud .

As the API economy enters its second decade, many organizations, from startups to Fortune 500, are recognizing the transformational power of APIs and creating game-changing applications. Developers now draw from a nearly infinite palette of internal and public APIs in an effort to outsource almost all of their application functionality.
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TensorKart: self-driving MarioKart with TensorFlow

TensorKart: self-driving MarioKart with TensorFlow

  • After playing way too much MarioKart and writing an emulator plugin in C , I managed to get some decent results.
  • When the plugin is loaded, the emulator checks for several function definitions and errors if any are missing.
  • With this in mind I played more MarioKart to record new training data.
  • Rabbit Hole – writing a mupen64plus input plugin
  • I started by modifying the TensorFlow tutorial for a character recognizer using the MNIST dataset .

Kevin Hughes’ Blog
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