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.
Continue reading “PyTorch tutorial distilled – Towards Data Science – Medium”

Free Machine Learning eBooks

Free #MachineLearning eBooks - March 2017 #abdsc

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

Free Machine Learning eBooks

Free Machine Learning eBooks - March 2017 | #DataScience #MachineLearning #RT

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

Free Machine Learning eBooks

Free #MachineLearning eBooks:  #abdsc #BigData #DataScience via @eelrekab

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
MACHINE LEARNING
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
Continue reading “Free Machine Learning eBooks”

GitHub

Introduction to Deep Learning for Image Recognition - SciPy US 2016 w/ slides |

  • The notebook accompanies the Introduction to Deep Learning for Image Recognition workshop to explain the core concepts of deep learning with emphasis on classifying images as the application.
  • The slides used for the workshop are available
  • Python data stack is used for the workshop.
  • Unsupervised learning using Autoencoders
  • Depending on time, the following topics might be covered

Read the full article, click here.


@YhatHQ: “Introduction to Deep Learning for Image Recognition – SciPy US 2016 w/ slides |”


scipyUS2016_dl-image – Introduction to Deep Learning for Image Recognition – SciPy US 2016


GitHub