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Peter Becar proslijedio/la je Tweet
First fastai v2 starter pack! :O (Note that v2 is very very pre-release -
@radekosmulski is an early adopter!)https://twitter.com/radekosmulski/status/1178793459446173701 …
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Peter Becar proslijedio/la je Tweet
Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented... http://arxiv.org/abs/1909.09148 pic.twitter.com/wvAXIJc5KM
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Peter Becar proslijedio/la je Tweet
Ensemble Methods for Machine Learning: AdaBoost https://buff.ly/2Lxuubu pic.twitter.com/Vdo0HoOm3t
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Peter Becar proslijedio/la je Tweet
Got an interesting research dataset and problem, but don't have the AI skill set you need in-house? Our new medical AI research institute can work with you to create a cross-domain team to test and develop your project. For free.https://www.prnewswire.com/news-releases/the-university-of-san-francisco-announces-the-launch-of-the-wicklow-ai-and-medical-research-initiative-300744264.html …
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Peter Becar proslijedio/la je Tweet
Wait what? Umm
@UniofOxford it seems that you guys have ripped off our entire course without providing any credit or mention or asked for permission? Could you please explain to the folks involved a little about standards of academic conduct? cc@FHIOxford (sponsors)https://twitter.com/JanLauGe/status/1050955083922452481 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Peter Becar proslijedio/la je Tweet
Kubernetes is great for deploying ML apps. Here's a detailed guide on how to use Kubernetes to deploy a Keras model as a REST API on Google Cloud:https://medium.com/analytics-vidhya/deploy-your-first-deep-learning-model-on-kubernetes-with-python-keras-flask-and-docker-575dc07d9e76 …
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Peter Becar proslijedio/la je Tweet
Wow
@karpathy just made my day at the@PyTorch conference: "I tell everyone to do http://fast.ai . There's nothing better out there." :) I still refer to his CS231n course notes all the time to figure out how best to explain things... http://cs231n.github.io/Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Peter Becar proslijedio/la je Tweet
Operational Machine Learning: Seven Considerations for Successful MLOps https://buff.ly/2KoYZ0V pic.twitter.com/2Jm4s7J5yT
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Peter Becar proslijedio/la je Tweet
Doing
#DataScience: A#Kaggle Walkthrough Cleaning Data#KDNhttp://ow.ly/HXy430kxOx9Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Peter Becar proslijedio/la je Tweet
http://fast.ai Deep Learning Part 2 Complete Course Notes https://buff.ly/2zLSy6D pic.twitter.com/XjwmEzypGx
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Peter Becar proslijedio/la je Tweet
Blog post on the Swimming Pool Detection and Classification demo showcased at
#esriuc. Joint@EsriGeoDev work with@DIVYANSHJHA using@ArcGISPro@arcgisonline and@jeremyphoward's@fastdotai.https://medium.com/geoai/swimming-pool-detection-and-classification-using-deep-learning-aaf4a3a5e652 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Peter Becar proslijedio/la je Tweet
Ouch: “prestigious institutions had on average 65% higher grant application success rates and 50% larger award sizes, whereas less-prestigious institutions produced 65% more publications and had a 35% higher citation impact per dollar of funding”https://buff.ly/2Ll28yN
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Peter Becar proslijedio/la je Tweet
a really solid blog post detailing the evolution of a fraud detection model at Lyft
tldr;
logistic regression -> xgboost -> NNs
this was driven by 1) need for more nuanced decision boundaries & 2) an increasing difficulty engineering useful features
https://eng.lyft.com/from-shallow-to-deep-learning-in-fraud-9dafcbcef743 …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Peter Becar proslijedio/la je Tweet
http://fast.ai Machine Learning Course Notes - a set of fantastic notes on the freely available http://fast.ai machine learning MOOC https://buff.ly/2KSX30p pic.twitter.com/MMhCLro7qE
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Peter Becar proslijedio/la je Tweet
5 Things You Need to Know about Reinforcement Learning https://buff.ly/2J2VAUH pic.twitter.com/zA7xMpaPjA
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Peter Becar proslijedio/la je Tweet
Time Series for Dummies – The 3 Step Process https://buff.ly/2IeaLtZ pic.twitter.com/lOeFRtrAAu
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Peter Becar proslijedio/la je Tweet
most common neural net mistakes: 1) you didn't try to overfit a single batch first. 2) you forgot to toggle train/eval mode for the net. 3) you forgot to .zero_grad() (in pytorch) before .backward(). 4) you passed softmaxed outputs to a loss that expects raw logits. ; others? :)
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Peter Becar proslijedio/la je Tweet
New post: "AdamW and Super-convergence is now the fastest way to train neural nets". Practically important new results from the amazing
@GuggerSylvain. Easily beats our winning DAWNBench approach! http://www.fast.ai/2018/07/02/adam-weight-decay/ …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Peter Becar proslijedio/la je Tweet
Our latest work demonstrates how agents achieve human-level performance in a complex first-person multiplayer game, and can even collaborate with human teammates! http://deepmind.com/blog/capture-the-flag …pic.twitter.com/UujmnrBXUz
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Čini se da učitavanje traje već neko vrijeme.
Twitter je možda preopterećen ili ima kratkotrajnih poteškoća u radu. Pokušajte ponovno ili potražite dodatne informacije u odjeljku Status Twittera.
Link to the NB on github: