This is Your Brain on Code 🧠💻🔢 computer programming is often associated with math, but researchers used functional MRI scans to show the role of the brain's language processing centers: https://lnkd.in/eN_-3RA

#datascience #machinelearning #ai #bigdata #analytics #statistics #artificialintelligence #datamining #computing #programmers #neuroscience

✴️ @AI_Python_EN

A nice explanation of backpropagation.
The notations are influenced by fast.ai (Deep Learning) program at USF and Deep Learning specialization course in Coursera.

https://lnkd.in/dthbv7U

#Deeplearning

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#SparkNLP: State of the Art Natural Language Processing
Spark NLP ships with many NLP features, pre-trained models and pipelines #johnsnowlab

NLP Features:
#Tokenization; #Normalizer; #Stemmer; #Lemmatizer; #RegexMatching; #TextMatching; #Chunking; #DateMatcher; #Part-of-speech tagging; #SentenceDetector; #SentimentDetection (ML model); #SpellChecker (ML and DL models); #WordEmbeddings (#BERT and #GloVe); #Namedentityrecognition; #Dependencyparsing (Labeled/unlabled); Easy #TensorFlow integration; #pretrainedpipelines!

Github: https://lnkd.in/fbWquan
Website: https://lnkd.in/fRqsDHX

✴️ @AI_Python_EN

Simpson's paradox and Interpreting data

"A trend or result that is present when data is put into groups that reverses or disappears when the data is combined"

It is interesting to face these kind of challenges when working on the data and it gets even more interesting when you have to find way to select the right data points to make some concrete decisions.

Have a look at this article.

Link - https://lnkd.in/fnHswjM

I hope this helps! Have a productive weekend.

✴️ @AI_Python_EN

Cool paper written by Yoshua Bengio’s MILA team.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Paper: arxiv.org/abs/1905.10437
GitHub for M4 dataset: https://lnkd.in/dWP5NmF
#timeseries #deeplearning

✴️ @AI_Python_EN

Human-level performance in first-person multiplayer games with population-based deep reinforcement learning

Jaderberg et al.: https://lnkd.in/eGTXYfE

#artificialintelligence #deeplearning #reinforcementlearning

✴️ @AI_Python_EN

#PyTorch Tensor To List: Convert a PyTorch Tensor To A Python List
http://bit.ly/2WfPPfK

✴️ @AI_Python_EN

Teaching language models grammar really does make them smarter

http://news.mit.edu/2019/teaching-language-models-grammar-makes-them-smarter-0529


✴️ @AI_Python_EN

Inferring a person’s looks from the way they speak: from a short input audio segment of a person speaking, the network directly reconstructs an image of the person’s face. Great: Clearly states ethical limits.
Paper: https://arxiv.org/pdf/1905.09773.pdf
Project: https://speech2face.github.io

✴️ @AI_Python_EN

Video: #AI, the end of #deeplearning ? #DeepLearning #QuantumMechanics #MachineLearning #CognitiveComputing #RPA #IntelligentAutomation

https://t.co/F5H5qfJeGk
✴️ @AI_Python_EN

check this collection of awesome deep Learning resources

https://github.com/frontbench-open-source/Data-Science-Free

✴️ @AI_Python_EN

THe best paper from ICLR 2019

https://www.analyticsvidhya.com/blog/2019/05/best-papers-iclr-2019/

Statistics, data science and jazz...

Though many jazz musicians have had extensive formal training in classical music - pianist Andre Previn being one notable example - some have been almost entirely self-taught - guitarist Wes Montgomery being one notable example.

Montgomery developed his own very unusual way of picking. If you're curious about what I mean, there are many videos on YouTube him performing.

He came from a musical family and he definitely had a talent for music. But the way he taught himself to play also had an influence on what he played, much of which was highly original, and his influence on jazz (and rock) continues to this day. He is generally regarded as one of the best guitarists ever.

What does this have to do with statistics and data science? Stats plays a vital role in data science, yet many data scientists are essentially self-taught or have learned from others who were largely self-taught.

I am not the only statistician concerned about misunderstandings and misuse of statistics in data science - Randy Bartlett, for one, has warned of a coming deluge of statistical malpractice. Some would argue that the deluge has arrived.

On the other hand, one wonders if data science will produce an equivalent of Wes Montgomery.

✴️ @AI_Python_EN

In a paper published recently, researchers from MIT’s Computer Science & Artificial Intelligence Laboratory have proposed a method for learning a face from audio recordings of that person speaking.

In their architecture, researchers utilize facial recognition pre-trained models as well as a face decoder model which takes as an input a latent vector and outputs an image with a reconstruction.

Paper: https://lnkd.in/fiUBjqh

#machinelearning #deeplearning #speech2face

✴️ @AI_Python_EN

Avoiding Backtesting Overfitting by Covariance-Penalties: an empirical investigation of the ordinary and total least squares cases
Researchers: Adriano Koshiyama, Nick Firoozye
Paper: https://lnkd.in/fWtth8W
#artificialinteligence
#machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Use of Artificial Intelligence Techniques / Applications in Cyber Defense
Researcher: Ensar Şeker
Paper: http://ow.ly/eqe450uukBx
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Learning Compositional Neural Programs with Recursive Tree Search and Planning

Paper: http://ow.ly/dEaX50uukqv

#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Cracking open the black box of automated machine learning

#MachineLearning
https://bit.ly/2HN9ETC

✴️ @AI_Python_EN

The code for "Learning Undirected Posteriors by Backpropagation through MCMC" is released. I had lots of fun working on this. The paper comes with in-depth discussion of possible future works, ideal for summer interns😉
paper http://bit.ly/2XkcSDJ
code http://bit.ly/2WetVup

✴️ @AI_Python_EN

Bagdasaryan and Shmatikov find that training using private SGD increases error disparities between over and under-represented groups. They blame gradient clipping, which has a larger effect on data points less like the average. An interesting fairness/privacy tradeoff.
Differential Privacy Has Disparate Impact on Model Accuracy.
http://arxiv.org/abs/1905.12101

✴️ @AI_Python_EN

Integrating TVM into PyTorch

https://tvm.ai/2019/05/30/pytorch-frontend

✴️ @AI_Python_EN