"Text generation using a RNN with eager execution"

Blog: https://lnkd.in/e3Xvneh

#ArtificialIntelligence #RecurrentNeuralNetworks #TensorFlow

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SageDB: A Learned Database System

Paper by Tim Kraska:
https://lnkd.in/eKZzqjQ

"GPUs will increase 1000× in performance by 2025, whereas Moore’s law for CPUs essentially is dead. By replacing branch-heavy algorithms with neural networks, the DBMS can profit from these hardware trends."

#artificialintelligence #database #deeplearning #machinelearning #neuralnetworks

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Have you ever wondered just why OpenCV has the fame and name that it has? Yes, the features are there but so is the speed. A month back I performed several tests to compare the performance of OpenCV and other deep learning frameworks on a CPU. You can check out the results at: Join Us
https://www.learnopencv.com/cpu-performance-comparison-of-opencv-and-other-deep-learning-frameworks/

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All materials of of CS188 Artificial Intelligence are now available

From UC Berkeley and Berkeley AI Research:
https://lnkd.in/eXJdv-R

#artificialintelligence #deeplearning #reinforcementlearning

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Unsupervised machine translation: A novel approach to provide fast, accurate translations for more languages

By Lample et al.

Paper: https://lnkd.in/d2eXdRS

Code: https://lnkd.in/d8Nk2nY

Blog: https://lnkd.in/d8hGtba

#deeplearning #machinelearning #unsupervisedlearning

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Auto-Keras

Auto-Keras is an open source software library for automated machine learning (AutoML).

GitHub: https://lnkd.in/eJAKXy5

#keras #deeplearning #machinelearning


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Deep Learning and Robotics

Slides by Pieter Abbeel: https://lnkd.in/eChDuNZ

#deeplearning #reinforcementlearning #robotics

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Neural Reading Comprehension and Beyond

By Danqi Chen: https://lnkd.in/eY_PreF

#artificialintelligence #deeplearning #machinelearning #research #thesis

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Looking for papers with code? If so, this GitHub repository, a clearinghouse for research papers and their corresponding implementation code, is definitely worth checking out.Join Us

#artificialintelligence #deeplearning #machinelearning #research #paper

https://www.kdnuggets.com/2018/12/papers-with-code-fantastic-github-resource-machine-learning.html

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Simple Alpha Zero

If you like our channel, i invite you to share it with your friends:

https://web.stanford.edu/~surag/posts/alphazero.html

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Interesting line of question by Y.Bengio about why quantum computing folks need to digitalize their systems:

https://www.youtube.com/watch?v=vfJuvNuSPKw

#quantumcomputing #deeplearning

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Topic Modeling with LSA, PSLA, LDA & lda2Vec – NanoNets

#deeplearning #machinelearning #artificialintelligence
https://bit.ly/2kxcq2Z

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Machine Learning models have a reputation for being difficult to explain. After all, they are, in some sense, sacrificing explainability for predictive accuracy.
In recent years, there's more interest in making ML models more explainable and, therefore, more trustworthy. The common example is that of a denied credit application: a customer would want to know why she has been rejected.
If you like our channel, i invite you to share it with your friends
LIME (locally interpretable model agnostic explanations) is method aimed at improving explainability. The following is a continuation of the LIME approach called Anchors. This presentation builds the intuition for it.

https://lnkd.in/ekJEqPz

#machinelearning #analytics #datascience #ml #statistics

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A Brief Survey of #Deep Reinforcement Learning

https://bit.ly/2rZSQAm

#مقاله
Deep Learning for Generic Object Detection: A Survey

https://bit.ly/2s3gw6M

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This #Algorithm Can Create 3D #Animations From A Single Still 2D Image.
A 2D subject in a single photo as input, and creates a 3D animated version of that subject. The animation can then walk out, run, sit, or jump in #3D.

#artificialintelligence #technology #research

https://lnkd.in/f8hxfcd #machinelearning
If you like our channel, i invite you to share it with your friends
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Pranoy made an #AI to automate colorizing black and white videos using #deeplearning with #pytorch.

Github: https://lnkd.in/fy7WeQ3
Pranoy provided a colab notebook to speed things up so feel free to check it out.

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The 10 Biggest datasets of 2018

0) Open Images V4 from Google AI on April 30th Contains 15.4M bounding-boxes for 600 categories on 1.9M images.
Paper: https://lnkd.in/fm4xiUm

1) MURA from Stanford University ML Group on May 24 Radiographic image dataset
Paper: https://lnkd.in/fBy5szB

2) BDD100K from BAIR, Georgia Tech, Peking University, Uber AI
on May 30 Self-Driving Car Dataset.
Paper: https://lnkd.in/f-sYj9k

3) SQuAD 2.0 from Stanford
on June 11 QA Dataset.
Paper: https://lnkd.in/fYc6c5W

4) CoQA from Stanford on August 21 QA Dataset
Paper: https://lnkd.in/fKvuTvE

5) Spider 1.0 from Yale Univ on September 24 Cross-domain semantic parsing and text-to-SQL dataset.
Paper: https://lnkd.in/fWyR2x8

6) HototQA from Carnegie, Stanford, and Montreal on September 25 QA Dataset on Wiki
Paper: https://lnkd.in/fTtTgZt

7) Tencent ML Images from Tencent AI Lab on Oct 18 largest open-source multi-label image dataset
Paper: https://lnkd.in/ffV6VD5

8) Tencent AI Lab Embedding Corpus for Chinese words and phrases on Oct 19 Embeddings Dataset
Paper: https://lnkd.in/ffV6VD5

9) fastMRI from NYU and Facebook AI on November 26
Knee MRI Images Dataset
Paper: https://lnkd.in/fQuUDNk

Read: https://lnkd.in/fXU9Kr6

#dataset #datasets

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Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach.
http://arxiv.org/abs/1812.11670
#RNN #ML

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Hough transform simplified! Hough Transform and Line Detection with #Python
https://youtu.be/G019Av7XhGo

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The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)

Blog by Jay Alammar: https://lnkd.in/ejqSjnZ

#NaturalLanguageProcessing #NLP #TransferLearning

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Style-based GANs – Generating and Tuning Realistic Artificial Faces

#ML #GAN

https://bit.ly/2R5wqN2

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