Facebook: Introducing Wav2latter++
#DataScience #MachineLearning #Artificialintelligence

http://bit.ly/2EP6spL

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Rules of Machine Learning: Best Practices for ML Engineering

By Martin Zinkevich: https://lnkd.in/d-g49zg

#ArtificialIntelligence #MachineLearning

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A tip for all prospective #datascientists: #datascience is nothing but a new means to solve #business problems. #problemsolving will be only key to success. Coding languages & technologies will keep changing. Problem solving skills will always be top priority for any recruiter.

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How to manage a Deep Learning team to improving an algorithm in production?

First of all, we split the process into a different part as pre-processing, choose the right model, algorithm implementation and integration.

1. In our team, there is a couple of people has a deep knowledge of the data. These persons can set up the training set in accord with the Deep Learning research that has the goal to find the best model in the literature for our specific problem.

2. The Deep Learning research has the assignment to understand if someone has faced the same problem in some kind of academic research. This is the best starting point to try to solve a real work problem. Bring academic research in an enterprise solution.

3. After trying to reproduce the same result of the academic research we start the real implementation of the algorithm. This means, hyperparameters implementation to find the best fit of our model.

4. The last part is very important. Once we find the best solution in the lab. We have to release this code in production. This is a critical part because sometimes the results are good in the lab but the model does not work very well in the real world.

#deeplearningteam

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Machine Learning in High Energy Physics Community White Paper"

Paper by Albertsson et al.: https://lnkd.in/ehNqt2k


#ComputationalPhysics #MachineLearning #HighEnergyPhysics

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A paper list of object detection using deep learning

GitHub: https://lnkd.in/e8USQmc

#deeplearning #machinelearning #objectdetection


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NLP 2018 Highlights
The highlights in this report are categorized by the following key topics: AI ethics, research publications, trends, education, resources, industry, and much more

https://github.com/omarsar/nlp_highlights

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Recognizing Speech Commands Using Recurrent Neural Networks with Attention
#ArtificialIntelligence #MachineLearning #DataScience

http://bit.ly/2R1yeGP

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"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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