Great starting point for PyTorch Reinforcement Learning projects and fantastic effort by Heinrich Küttler &

for reproducible RL research! "Why PyTorch?" you might ask.

Announcing TorchBeast, an IMPALA-inspired pytorch

platform for distributed RL research. Used in a growing number of projects here at FacebookAI

Paper:
https://arxiv.org/abs/1910.03552
Code:
https://github.com/facebookresearch/torchbeast

❇️ @AI_Python_EN

Generalized Inner Loop Meta Learning, aka Gimli
https://arxiv.org/abs/1910.01727

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In parallel with this paper, FacebookAI

has released higher, a library for bypassing limitations to taking higher-order gradients over an optimization process.
Library:
https://github.com/facebookresearch/higher
Docs:
https://higher.readthedocs.io

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Yoshua Bengio, one of the pioneers of deep learning, now wants to his algorithms to ask 'why' things happen:

https://www.wired.com/story/ai-pioneer-algorithms-understand-why/

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Introducing sotabench : a new service with the mission of benchmarking every open source ML model. We run GitHub repos on free GPU servers to capture their results: compare to papers, other models and see speed/accuracy trade-offs. Check it out:

https://sotabench.com

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With 180+ papers mentioning
Transformers and its predecessors, it was high time to put out a real paper that people could cite.

https://arxiv.org/abs/1910.03771

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Microsoft Open Source Engineer pythiccoder

explores nine advanced tips for production #ML. Read:

https://medium.com/microsoftazure/9-advanced-tips-for-production-machine-learning-6bbdebf49a6f

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Why deep-learning AIs are so easy to fool
https://nature.com/articles/d41586-019-03013-5

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Spooky Lavanya

Weights & Biases is officially included in Stanford's CS 197 class!

I wrote a quick tutorial on how to train a neural network using #PyTorch & track your experiments in W&B!
Class:
http://cs197.stanford.edu/assignments/a3.shtml
Code:
https://colab.research.google.com/drive/1zkoPdBZWUMsTpvA35ShVNAP0QcRsPUjf

#MachineLearning

❇️ @AI_Python_en

Building a neural network can be confusing! I wrote a guide to help you navigate the treacherous NN waters using
Highly rec forking the kernel & playing with the code!
Post:
https://lavanya.ai/2019/08/10/training-a-neural-network-start-here/
Code:
https://kaggle.com/lavanyashukla01/training-a-neural-network-start-here

#MachineLearning #DataScience

❇️ @ai_python_en

Course 1 : A Learning Path to become Data Scientist in 2019
Link :
https://bit.ly/2HOthei

Course 2 : Experiments with Data
Link :
https://bit.ly/2HQuQbw

Course 3 : Python for Data Science
Link :
https://bit.ly/2HOG5RG

Course 4 : Twitter Sentiments Analysis
Link :
https://bit.ly/2HR8O8A

Course 5 : Creating Time Series Forecast with Python
Link :
https://bit.ly/2XniU6r

Course 6 : A path for learning Deep Learning in 2019
Link :
https://bit.ly/2HO1VVJ

Course 7 : Loan Prediction Practice problem
Link :
https://bit.ly/2IcynQl

Course 8 : Big mart Sales Problem using R
Link :
https://bit.ly/2JUlZIb

❇️ @AI_Python_EN

According to common belief, neural networks' main advantage over traditional ML algorithms is that NNs learn features by themselves while in the traditional ML, you handcraft features. This is not exactly true. Well, it's true for vanilla feed-forward NNs consisting only of fully connected layers. But those are very hard to train for high dimensional inputs like images.

When you use a convolutional neural network, you already use two types of handcrafted features: convolution filters and pooling filters.

The designer of the convolutional NN for image classification has looked into the input data (this is what traditional ML engineers do to invent features) and decided that patches of pixels close to each other contain information that could help in classification, and at the same time reduce the number of NN parameters.

The same reasoning is used when we classify texts using bag-of-words features. We look at the data and decide that individual words and n-grams of words would be good features to classify a document. This reduces the number of input features while allowing us to accurately classify documents.

BTW, the way convolutional filters apply (sum of element-wise multiplications, spanning over channels resulting in one number) is a hell of a feature!
Burkov

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Google’s SummAE generates abstract summaries of paragraphs
#DataScience #MachineLearning #ArtificialIntelligence

http://bit.ly/2pVMjZJ

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.

just published my (free) 81-page guide on learning #ComputerVision, #DeepLearning, and #OpenCV!
Includes step-by-step instructions on:
- Getting Started
- Face Applications
- Object Detection
- OCR
- Embedded/IOT
- and more!
Check it out here:
http://pyimg.co/getstarted
And if you liked it, please do give it a share to spread the word. Thank you!
#Python #Keras #MachineLearning #ArtificialIntelligence #AI

❇️ @AI_Python_EN

The war between ML frameworks has raged on since the rebirth of deep learning. Who is winning? Horace He data analysis shows clear trends: PyTorch is winning dramatically among researchers, while Tensorflow still dominates industry.
#PyTorch #Tensorflow

https://thegradient.pub/state-of-ml-frameworks-2019-pytorch-dominates-research-tensorflow-dominates-industry/

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.


Free Book: Deep Learning and Computer Vision with CNNs

https://www.datasciencecentral.com/profiles/blogs/free-book-deep-learning-and-computer-vision-with-cnns

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If you're interested in using pytorch on free Colab TPUs, here are some notebooks to get you started

https://github.com/pytorch/xla/tree/master/contrib/colab

❇️ @AI_Python_EN

Automatically generate floor plans designs.
https://t.co/WEMrTKCI86
https://t.co/GKxF2a7H72

Image Quality Assessment for Rigid Motion Compensation.
https://t.co/3TC1f2fst3
https://t.co/ZpwS8PIYGj