Interesting paper! Tensorflow 2.0 and PyTorch 1.1 already pushed the language to the limits of what it can do. As Julia and Swift mature their support for #deeplearning, we may need to switch
https://buff.ly/320IH76

✴️ @AI_Python_EN
🗣 @AI_Python_arXiv

A Review of “Compound Probabilistic Context-Free Grammars for Grammar Induction”

By Ryan Cotterell

https://lnkd.in/fVVvwud
paper https://lnkd.in/fr-U2vK

#MachineLearning
#NaturalLanguageProcessing #NLP

✴️ @AI_Python_EN

New Google Brain Optimizer Reduces BERT Pre-Training Time From Days to Minutes
http://bit.ly/30tZfDN
#AI #MachineLearning #DeepLearning #DataScience

✴️ @AI_Python_EN

A mathematical theory of semantic development in deep neural networks

https://lnkd.in/ejt9fe6

#MachineLearning #ArtificialIntelligence #Neurons #Cognition

✴️ @AI_Python_EN

This is the reference implementation of Diff2Vec - "Fast Sequence Based Embedding With Diffusion Graphs" (CompleNet 2018). Diff2Vec is a node embedding algorithm which scales up to networks with millions of nodes. It can be used for node classification, node level regression, latent space community detection and link prediction. Enjoy!

https://lnkd.in/dXiy5-U

#technology #machinelearning #datamining #datascience #deeplearning #neuralnetworks #pytorch #tensorflow #diffusion #Algorithms

✴️ @AI_Python_EN

Hey #DeepLearning #AI enthusiast, have you heard of this cool drag & drop AI from #DSAIL lab from MIT?

This is an amazing tool which #managers and #datascience professionals can use instantly!

The researchers evaluated the tool on 300 real-world datasets. Compared to other state-of-the-art #AutoML systems, VDS’ approximations were as accurate, but were generated within seconds, which is much faster than other tools, which operate in minutes to hours.

Next they want to add features like alerts users to potential data bias or errors. For example, to protect patient privacy, sometimes researchers will label medical datasets with patients aged 0 (if they do not know the age) and 200 (if a patient is over 95 years old). But beginners may not recognize such errors, which could completely throw off their analytics.

Here is link to their project Northstar
https://lnkd.in/dmHQugW

Take a look! This is pretty awesome.
#artificialintelligence #automation #autoML #visualization

✴️ @AI_Python_EN

💡 What is the bias-variance trade-off?

Bias refers to an error from an estimator that is too general and does not learn relationships from a data set that would allow it to make better predictions.

Variance refers to error from an estimator being too specific and learning relationships that are specific to the training set but will not generalize to new observations well.

👉 In short, the bias-variance trade-off is a the trade-off between underfitting and overfitting. As you decrease variance, you tend to increase bias. As you decrease bias, you tend to increase variance.

👉 Generally speaking, your goal is to create models that minimize the overall error by careful model selection and tuning to ensure sure there is a balance between bias and variance: general enough to make good predictions on new data but specific enough to pick up as much signal as possible.

#datascience

✴️ @AI_Python_EN

Generating adversarial patches against YOLOv2

Adversarial attacks on machine learning models have seen increasing interest in the past years. By making only subtle changes to the input of a convolutional neural network, the output of the network can be swayed to output a completely different result. The first attacks did this by changing the pixel values of an input image slightly to fool a classifier to output the wrong class.

paper: https://lnkd.in/d5SnGYv

#yolo #deeplearning

✴️ @AI_Python_EN

Swift Core ML 3 implementation of BERT for Question answering

Built Julien Chaumond, Lysandre Debut and Thomas Wolf at Hugging Face: https://lnkd.in/ejJabYh

#machinelearning #naturallanguageprocessing #nlp

✴️ @AI_Python_EN

AI Simulates The Universe And Not Even Its Creators Know How It's So Accurate!

For the first time, scientists have used artificial intelligence to create complex, three-dimensional simulations of the Universe.

It's called the Deep Density Displacement Model, or D3M, and it's so fast and so accurate that the astrophysicists who designed it don't even know how it does what it does!

What it does is accurately simulate the way gravity shapes the Universe over billions of years. Each simulation takes just 30 milliseconds - compared to the minutes it takes other simulations.

Paper: https://lnkd.in/dMJgJRb
#deeplearning #machinelearning

✴️ @AI_Python_EN

VideoBERT: A Joint Model for Video and Language Representation Learning

Sun et al.: https://lnkd.in/ek7MYKP

#ComputerVision #PatternRecognition #ArtificialIntelligence

✴️ @AI_Python_EN

Google announced the updated YouTube-8M dataset

Updated set now includes a subset with verified 5-s segment level labels, along with the 3rd Large-Scale Video Understanding Challenge and Workshop at #ICCV19.

Link: https://lnkd.in/f_6Jb7Y

#DL #datasets

✴️ @AI_Python_EN

*Fine-Grained Zero-Shot Recognition with Metric Rescaling*
https://arxiv.org/abs/1906.11892

✴️ @AI_Python_EN

Training an AI agent to play Snake with TensorFlow 2.0.

Code by Paweł Kieliszczyk: https://lnkd.in/edTVYEC

#MachineLearning #ReinforcementLearning #TensorFlow

✴️ @AI_Python_EN

StarAi: Deep Reinforcement Learning Course provides easy to use exercises, with answers, to reinforce our learning.

Link: https://lnkd.in/fYQUVJs

StarAi Starcraft Google Colaboratory IPython Notebook
https://lnkd.in/fv_siQY
Author: Frank He

StarAi tutorial on how to setup your Starcraft machine learning model for Colaboratory
https://lnkd.in/fBEHRPN
Author: Paul Conyngham

StarAi: “Foundations” modular Reinforcement Learning Framework
https://lnkd.in/fH2pAQX
Author: William Xu

✴️ @AI_Python_EN

Generating content using AI and Machine learning

GANs are generating different types of contents and probably we all have seen many examples:
1- Videos: This is how you can do gans yourself
https://lnkd.in/gZeB8sY

2-Music: https://lnkd.in/gsxuaMb
3-Text: https://lnkd.in/gu8fWrh
4-Image: https://lnkd.in/gDTwDNU
Yet, as far as I know, no one is currently getting paid for using GANs (except for deepfake!). Please comment if you know any?

Apart from GANs, AI is generating content using other Machine learning techniques (+ heuristics) e.g. Natural Language Generation.
Presentation slides:
https://lnkd.in/g996v-r


#artificialintelligence #datascience #machinelearning

✴️ @AI_Python_EN

#imbalancedData
What is it?
Ans-> Suppose, you are having a Classification problem with 2M records. The Output variable is having 2 categories (Yes- 500, No- 1.99M or more).

This is the imbalanced data, as one category is far less than the other category in the Output variable.

Examples-> Credit Card fraud, Cancer Detection(or any other disease that is severe), and many more.

How to deal with it?
1) Undersampling
2) Oversampling

#datascience #dataanalysis #learning

✴️ @AI_Python_EN

“We don’t like deep learning, because it is a black box and we cannot explain it.” Really? How many people actually know how their car engine, refrigerator or laptop works? How many people actually know how spaghetti or fried chicken are made? These are common everyday black boxes. And we feel totally comfortable using them every day even though we have no clue how they work. So what is the difference? Why it is okay to drive a car but not okay to use deep learning model? Because EVERYONE drives a car and not EVERYONE uses deep learning. That’s the only difference.

DL can be made less black box when needed. (If we're classifying images of felines, I'm not sure how transparent they need to be except to the developers.) Charu Aggarwal of IBM has suggested that gradients can be used to interpret the size and direction of predictions resulting from a change in an input. Sensitivity analysis, as used by statisticians, can be used in similar ways, as can statistical analysis of the DL's predictions.


Negative. All those other examples you listed have readily available experts that understand how it all works. In Deep Learning, even the brightest people who created a neural network have a difficult time explaining why their creation is working/not working. Neural networks are by definition convoluted.

Also, deep learning is often impractical and has very few applications.

https://link.medium.com/lsmcXVn8WX
✴️ @AI_Python_EN

Here is an exclusive video that highlights the 5 things that any aspirant should consider before choosing a Machine Learning course. Watch the full video here:
https://lnkd.in/fVyW5Uq



#machinelearning #artificialintelligence #datascience #deeplearning #datascientist #ai

✴️ @AI_Python_EN

A simple tutorial on how to train a Transformer model from scratch using TensorFlow GPU in Docker. It's based on the official implementation from the TF GitHub... But using Docker!
https://blog.exxactcorp.com/examining-the-transformer-architecture-part-3-training-a-transformer-network-from-scratch-in-docker/

✴️ @AI_Python_EN

Deep RL agents are data hungry and often learn task-specific representations. Our model learns object-centric abstractions from raw videos. This enables highly data-efficient RL and structured exploration.
https://arxiv.org/abs/1906.11883

✴️ @AI_Python_EN