François Chollet

This is how you implement a network in Chainer. Chainer, the original eager-first #deeplearning framework, has had this API since launch, in mid-2015. When PyTorch got started, it followed the Chainer template (in fact, the prototype of PyTorch was literally a fork of Chainer).

Nearly every day, I am getting ignorant messages saying, "PyTorch is an original innovation that TensorFlow/Keras copied". This is incorrect. Subclassing is a fairly obvious way to do things in Python, and Chainer had this API first. Many others followed.

I had been looking at adding a Model subclassing API to Keras as soon as late 2015 (before the Functional API even existed, and over a year before being aware of PyTorch), inspired by Chainer. Our first discussions about adding an eager execution mode also predate PyTorch.

By the time #PyTorch came out, I had been looking at its API (which is exactly the Chainer API) for 1.5 year (since the release of Chainer). It wasn't exactly a shock. There was nothing we didn't already know.



To be clear, it's a good thing that API patterns and technical innovations are cross-pollinating among deep learning framework. The #Keras API itself has a had a pretty big influence over libraries that came after. It's completely fine, and it all benefits end users.

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#PyTorch Tensor To List: Convert a PyTorch Tensor To A Python List
http://bit.ly/2WfPPfK

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We are back with a new blog post for our PyTorch Enthusiasts. If you are new to this field, Semantic Segmentation might be a new word for you.
Simply put it is an image analysis task used to classify each pixel in the image into a class which is exactly like solving a jigsaw puzzle and putting the right pieces at the right places!

Today's blog by Arunava Chakraborty is about Semantic Segmentation using torchvision and will help explore more about this interesting topic.

https://lnkd.in/gG5fW3M

#Semantic #segmentation #torchvision #PyTorch #ai #deeplearning #machinelearning #computervision #opencv

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Practical Deep Learning with Bayesian Principles
Osawa et al.: https://arxiv.org/pdf/1906.02506.pdf
#Bayesian #DeepLearning #PyTorch #VariationalInference

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Why 2019 is the year of Knowledge Graphs?
✔️#Knowledgegraph became a centerpiece of #Accentur and #Microsoft ’s toolkits.

✔️Knowledge graph lessons from Google, #Facebook, #eBay, #IBM.

✔️Graph algorithms and analytics by #Neo4j, #Nvidia and #AWS.

More about the why?
https://lnkd.in/g87BTrH

💥Great resources to get some hands-on experience:

✅ Implementing Knowledge Graphs in #Enterprises:
https://lnkd.in/ghisXMw

✅ How #Google’s Knowledge Graph Updates Itself:
https://lnkd.in/gayCpPw

✅ Extracting knowledge from knowledge graphs using #Facebook #Pytorch BigGraph.
https://lnkd.in/gHgj6AH

✅ The Data Fabric for #MachineLearning : #DeepLearning on Graphs. By Favio Vazquez
https://lnkd.in/gsCnTTM

✅ Why Knowledge Graphs Are Foundational to #ArtificialIntelligence
https://lnkd.in/g5WVARe

Absolutely essential for data scientists to upskill themselves, Knowledge Graphs are coming...
#datascience #AI

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Getting System Information in Linux using Python Script.
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux

http://bit.ly/2X56cZa

✴️ @AI_Python_EN

This is the reference implementation of Diff2Vec - "Fast Sequence Based Embedding With Diffusion Graphs" (CompleNet 2018). Diff2Vec is a node embedding algorithm which scales up to networks with millions of nodes. It can be used for node classification, node level regression, latent space community detection and link prediction. Enjoy!

https://lnkd.in/dXiy5-U

#technology #machinelearning #datamining #datascience #deeplearning #neuralnetworks #pytorch #tensorflow #diffusion #Algorithms

✴️ @AI_Python_EN

PyTorchPipe (PTP)

A component-oriented framework for rapid prototyping and training of computational pipelines combining vision and language:
https://lnkd.in/ehJbseR

#PyTorch #NeuralNetworks #DeepLearning

✴️ @AI_Python_EN