Automated theorem prover driven by deep reinforcement learning: DeepHOL. Comes with a benchmark suite of 29,462 theorems to be proven. It can already prove 58% of them using 41"tactics".

PDF: https://arxiv.org/pdf/1904.03241.pdf

✴️ @AI_Python_EN

I wanna be a data scientist, but… how!?

https://link.medium.com/CUDoPvMOEV
#DataScience #ArtificialIntelligence #MachineLearning #DeepLearning #DataScientist


✴️ @AI_Python_EN

A thread of research that I've been particularly excited about lately is the linearized training of neural networks and the Neural Tangent Kernel. To that end, we're releasing code - written in JAX - that we've been using for our research:
https://github.com/google/neural-tangents

✴️ @AI_Python_EN

Useful #Linux cheat sheet for #datascientists

✴️ @AI_Python_EN

A great GitHub repository with tutorials on getting started with #PyTorch and TorchText for #sentimentanalysis in #Jupyter Notebooks. What a great resource!

https://github.com/bentrevett/pytorch-sentiment-analysis

✴️ @AI_Python_EN

#Datascience needs to move beyond #research to actually make a real impact in the #AI economy.

Agree?

#DeepLearning #artificialintelligence #machinelearning

✴️ @AI_Python_EN

Here is a list of handy tools to keep in your #DataScience toolbox:

- - -
➤ Data Science Platform (All-in-one Packages & IDE)
Anaconda - https://lnkd.in/gWHY_ij

➤ Programming Languages (Python, R, and SQL)
Python Zero-to-Hero
https://lnkd.in/gEyZd5W
SQL for Data Science
https://lnkd.in/gjvgdhZ
(https://lnkd.in/fZxEF-g)

➤ Data Science Libraries
Top 15 Python Libraries (SciKit-Learn, TensorFlow, NLTK, matplotlib, etc..)
https://lnkd.in/gw_f3Ga

➤ Distributed Systems (Spark, Hadoop, Kafka)
Spark - https://lnkd.in/gC92A64
Hadoop - https://lnkd.in/gKuxgwx
Kafka - https://lnkd.in/gBB9Ja7

➤ Version Control (Git)
https://lnkd.in/g5sJj2H

➤ Reproducibility and Virtual Machines (Docker)
https://lnkd.in/gzYjuuA

➤ Cloud Services (AWS, Google Cloud, Microsoft Azure)
https://lnkd.in/gBJeQuY

➤ Serverless Architecture (Firebase)
https://lnkd.in/gbB6eeM

Data Warehouse and Data Lake
https://lnkd.in/gepNRMw


- - -
This list contains a high-level overview of the many tools out there that can be used for Data Science.

It's always great to refer back to your tools and keep things in check.

Hope this helps 🙂

✴️ @AI_Python_EN

The Full Stack #DeepLearning Bootcamp was a lot of fun in person, but of course not everyone can make it in person. Very excited to start releasing the materials today, here:

https://lnkd.in/giizppb

Happy learning from home!

✴️ @AI_Python_EN

Haha so funny! A typical day in the life of a machine learner 😂 #deeplearning #machinelearning #fun

✴️ @AI_Python_EN

Weakly Supervised Gaussian Networks for Action Detection
Researchers: Basura Fernando, Cheston Tan Yin Chet, Hakan Bilen
Paper: http://ow.ly/NC0S50qAP7S

#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Good start on mastering a new skill from scratch.

some of covered topics are :
1) Careers (complete learning paths to become a master)
2) Topics (comprehensive guides about a specific topic)
3) Tools
4) Reasearch

https://github.com/clone95/Virgilio

#machinelearning #AI #getting_started
@AI_Python_EN

Starting weights can matter a lot for training a neural net. Read this deeplearning.ai tutorial on initializing your neural network:
http://bit.ly/2XmzHGu

✴️ @AI_Python_EN

Wow nice there's a new convolution operation for CNNs called "Octave Convolution (OctConv)" which can be used as a direct replacement of plain vanilla convolutions without any adjustments in the network architecture. The idea of OctConv is pretty cool. In images, information is conveyed at different frequencies i.e. high frequencies show fine details whereas low-frequencies show global structures.

The idea then is to factorize the feature maps into a high-frequency/low-frequency feature maps and then reduce the spatial resolutions of the low-frequency maps by an octave. This not only leads to lower memory/computation cost but also to better evaluation results such as accuracy in an image classification task. Can't wait to see this in Keras/TensorFlow! #deeplearning #machinelearning

Paper: https://lnkd.in/dckWSDq

✴️ @AI_Python_EN

Ian Goodfellow: Generative Adversarial Networks (GANs)
This conversation with Ian led me to rethink the way I see several basic ideas in deep learning, including generative models, adversarial learning, and reasoning. I definitely enjoyed it and hope you do as well.

Ian Goodfellow: Generative Adversarial Networks (GANs)

✴️ @AI_Python_EN

17 equations that changed the world
✴️ @AI_Python_EN

#DeepLearning is fun when you have loads of GPUs!

Here's a 256GB , 8 GPU cluster we will soon be testing as well.

#gpu #nvidia #research
#machinelearning
✴️ @AI_Python_EN

✴️ @AI_Python_EN

Stanford ML Group just released knee injury dataset they're calling MRNET.

Paper: https://lnkd.in/dwik_zz

Dataset: https://lnkd.in/dwS96AD
#ml #knee #injury #stanford #dataset #deeplearning

https://lnkd.in/dDpD38u

✴️ @AI_Python_EN

Neural Painters: A learned differentiable constraint for generating brushstroke paintings

Nice paper combining ideas from world models and style transfer

paper: https://lnkd.in/gpWm3y9

github: https://lnkd.in/gVuExnm


✴️ @AI_Python_EN

Great explanation of permutation test.
Should alpacas be shampooed? ;-)

https://lnkd.in/eXqA7ze

✴️ @AI_Python_EN

What make a company don't want spend on #AI development?

A. Don't have budget
B. Don't understand what is AI
C. Just stingy in general
D. Talent shortage
E. No satisfying consultant/vendor in their area
F. Other, ...

I'm courious about your experience, please choose, if you have multiple reason please start from most relevant one.

✴️ @AI_Python_EN