Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations

http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html

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Free COURSE. CS Deep Reinforcement Learning UC Berkeley

Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..

Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/

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Welcome to TensorWatch

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key visualizations of your models and data.

https://github.com/microsoft/tensorwatch/

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Group For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing

https://t.me/joinchat/Ly1-vFOq9aR4mjpIDwzoHA

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ice collection of PyTorch (and some TF) Jupyter notebooks for everything deep learning by Sabastian Raschka. https://github.com/rasbt/deeplearning-models

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Nice to see our World Models paper used to teach a lecture on Representation Learning in Reinforcement Learning as part of Berkeley’s course on Deep Unsupervised Learning.

They described the paper as “the simplest thing that can be done. I wouldn’t have expected it to work so well.” 🍰

https://lnkd.in/gjH3gHU
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ML Resources

By Sam Finlayson: https://lnkd.in/dArtQB8

#ArtificialIntelligence #DeepLearning #MachineLearning

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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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If you are into statistical analysis, don't miss this paper on variable selection!

#statistical_analysis #regression #variable_selection #model_building #epidemiology
https://onlinelibrary.wiley.com/doi/pdf/10.1002/bimj.201700067
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MelNet: A Generative Model for Audio in the Frequency Domain

Sean Vasquez and Mike Lewis: https://lnkd.in/dp36Nwk

Blog: https://lnkd.in/dnEacxY

#ArtificialIntelligence #DeepLearning
#MachineLearning

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Watch the Mona Lisa turn her head, in today's edition of stupid StyleGAN encoder tricks... #StyleGAN https://github.com/pbaylies/stylegan-encoder

Can we learn to detect objects without any supervision? Yes, if we assume that an object is a part of an image that can be redrawn while keeping the image realistic. With Mickael Chen and Thierry Artieres - https://arxiv.org/abs/1905.13539

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Deep reinforcement learning algorithms are impressive, but only when they work. In reality, they are largely unreliable and can yield very different results. larocheromain proposes two ways to achieve reliability in RL: https://aka.ms/AA5ann9 #ICML2019

The Enigma of Neural Text Degeneration as the First Defense Against Neural Fake News
If you want a sneek-peek in Yejin Choinka,and co-workers work on GROVER (a 1.5 billion param GPT-2-like model), check this live tweet 👇 Interesting hints, results, and analysis!
Paper: https://arxiv.org/abs/1905.12616
Demo: http://rowanzellers.com/grover/

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Keras notebooks


Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)

ConvNets: colab notebook with functions for constructing #keras models. Models:

AlexNet
VGG
Inception
MobileNet
ShuffleNet
ResNet
DenseNet
Xception
Unet
SqueezeNet
YOLO
RefineNet

https://github.com/Machine-Learning-Tokyo/DL-workshop-series

✴️ @AI_Python_EN

Looking to enhance your NLP skills but unfamiliar with mathematics and linguistic structures ?!

Statistical Natural Language Processing by Manning Schuetze covers :
1) Mathematical foundations

2) Linguistic essentials

3) Corpus-Based work

4) Most useful clustering models in supervised and unsupervised methods

5) Lexical Acquisition

and so much more !




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We're now available via #linkedin :)

link :
https://www.linkedin.com/groups/13723396/


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We have an opening for a post-doc position in my lab (http://fias.uni-frankfurt.de/en/neuro/triesch) at the Frankfurt Institute for Advanced Studies (FIAS) to study Open-Ended Deep Reinforcement Learning in simulated robots. We study systems that effectively learn to control their bodies and their environment by defining their own learning goals, practicing the skills for achieving these goals and setting themselves progressively harder goals. The work will be performed in the context of the European GOAL-Robots project (http://www.goal-robots.eu). For a quick overview of the project, check out this video:https://youtu.be/sordZmyp8u8. The focus of this post-doc position will be on simulated humanoids learning visually guided object interaction.

We are seeking an outstanding and highly motivated post-doc for this project. Applicants should have obtained a PhD in Machine Learning, Robotics, or a closely related field. The ideal candidate will have excellent programming and analytic skills and a broad knowledge of machine learning, robotics, and vision. An interest in cognitive development in human infants is a plus.

The Frankfurt Institute for Advanced Studies (https://fias.institute/en/) is a research institution dedicated to fundamental theoretical research in various areas of science. The city of Frankfurt is the hub of one of the most vibrant metropolitan areas in Europe. It boasts a rich culture and arts community and repeatedly earns high rankings in worldwide surveys of quality of living.

Applications should be sent as a single pdf file to triesch@fias.uni-frankfurt.de. Please include a brief statement of research interests, CV, and contact information for at least two references. The position can be filled immediately and applications will be reviewed on a continuing basis. The initial appointment will be for one year.

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Computational Narrative Intelligence and the Quest for the Great Automatic Grammatizator
Slides by Mark Riedl: https://www.dropbox.com/s/2o8enj7amaxxx1y/naacl-nu-ws.pdf?dl=0
#ArtificialIntelligence #MachineLearning #NaturalLanguageProcessing

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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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All 1,294 papers at #CVPR2019
Index: http://openaccess.thecvf.com/content_CVPR_2019/html/
#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN