Facebook have created and now open-sourced Nevergrad, a Python3 library that claims making easier to perform gradient-free optimizations.
Link: https://code.fb.com/ai-research/nevergrad/
Github: https://github.com/facebookresearch/nevergrad
Link: https://code.fb.com/ai-research/nevergrad/
Github: https://github.com/facebookresearch/nevergrad
Engineering at Meta
Nevergrad: An open source tool for derivative-free optimization
We are open-sourcing Nevergrad, a Python3 library that makes it easier to perform gradient-free optimizations used in many machine learning tasks.
Forwarded from NLP
Facebook has released #PyText — new framework on top of #PyTorch.
This framework is build to make it easier for developers to build #NLP models.
Link: https://code.fb.com/ai-research/pytext-open-source-nlp-framework/
This framework is build to make it easier for developers to build #NLP models.
Link: https://code.fb.com/ai-research/pytext-open-source-nlp-framework/
Engineering at Meta
Open-sourcing PyText for faster NLP development
We are open-sourcing PyText, a framework for natural language processing. PyText is built on PyTorch and it makes it faster and easier to build deep learning models for NLP.
Forwarded from IT jokes and more
Forwarded from NLP
4_6026376433876599718.pdf
6.4 MB
Deep learning cheatsheets, covering content of Stanford’s CS 230 class.
CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
#cheatsheet #Stanford #cnn #rnn #tipsntricks #dnn
CNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-convolutional-neural-networks
RNN: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks
TipsAndTricks: https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks
#cheatsheet #Stanford #cnn #rnn #tipsntricks #dnn
stanford.edu
CS 230 - Convolutional Neural Networks Cheatsheet
Teaching page of Shervine Amidi, Adjunct Lecturer at Stanford University.
Happy New Year! May 2019 be better than 2018 but not as good as 2020 😁
While I never professionally worked on graph based machine learning problems, they have always been fascinating and I have tried keeping up to date with newish papers. Today I came across a really nice package called AmpliGraph (https://lnkd.in/gpXYuuQ), available on pip and running on top of TensorFlow. The API looks very clean with a number of example notebooks. Excited to play around with this.
#machinelearning #ML #datascience #graphs #MLLM #Cubonacci #AI
#machinelearning #ML #datascience #graphs #MLLM #Cubonacci #AI
New tutorial!🚀
Learn how to build an Image Hashing Search Engine that scales to 1,000,000s of images using #OpenCV, #Python, and VP-Trees.
Full tutorial w/ code here: http://pyimg.co/myj11 👍 #ComputerVision #MachineLearning #ArtificialIntelligence #DataScience #AI #BigData
Learn how to build an Image Hashing Search Engine that scales to 1,000,000s of images using #OpenCV, #Python, and VP-Trees.
Full tutorial w/ code here: http://pyimg.co/myj11 👍 #ComputerVision #MachineLearning #ArtificialIntelligence #DataScience #AI #BigData
PyImageSearch
Building an Image Hashing Search Engine with VP-Trees and OpenCV - PyImageSearch
In this tutorial, you will learn how to build a scalable image hashing search engine using OpenCV, Python, and VP-Trees.
Awesome Graph Classification
A collection of important graph embedding, classification and representation learning papers with implementations.
GitHub, by Benedek Rozemberczki: https://lnkd.in/eErZBnh
#graph2vec #deepgraphkernels #graphattentionmodel
#graphattentionnetworks
A collection of important graph embedding, classification and representation learning papers with implementations.
GitHub, by Benedek Rozemberczki: https://lnkd.in/eErZBnh
#graph2vec #deepgraphkernels #graphattentionmodel
#graphattentionnetworks
GitHub
benedekrozemberczki/awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification
"Mathematics For Machine Learning"
A book that is intended to help people understand the #mathematics behind the #MachineLearning techniques.
Its aim is to make people understand what goes under the hood in common ML algorithms.
The best part is that the team is also working on Jupyter notebook tutorials
Download the PDF of the book: https://lnkd.in/e-gXPRf
100% OFF in Home Delivery Asia 2019>>> https://lnkd.in/f_TxgKN
For Data Science Implementations:
Know Data Science https://lnkd.in/fMHtxYP
Understand How to answer Why https://lnkd.in/f396Dqg
Machine Learning Terminology https://lnkd.in/fCihY9W
Understand Machine Learning Implementation https://lnkd.in/f5aUbBM
Machine Learning on Retail https://lnkd.in/fihPTJf
and Marketing https://lnkd.in/fBncKiy
A book that is intended to help people understand the #mathematics behind the #MachineLearning techniques.
Its aim is to make people understand what goes under the hood in common ML algorithms.
The best part is that the team is also working on Jupyter notebook tutorials
Download the PDF of the book: https://lnkd.in/e-gXPRf
100% OFF in Home Delivery Asia 2019>>> https://lnkd.in/f_TxgKN
For Data Science Implementations:
Know Data Science https://lnkd.in/fMHtxYP
Understand How to answer Why https://lnkd.in/f396Dqg
Machine Learning Terminology https://lnkd.in/fCihY9W
Understand Machine Learning Implementation https://lnkd.in/f5aUbBM
Machine Learning on Retail https://lnkd.in/fihPTJf
and Marketing https://lnkd.in/fBncKiy
Detecting new knowledge in unstructured text using ML. More evidence that when you put large amounts of papers and reports together and apply OpenSource machine learning to the text - the whole can be greater than the sum of its parts. This paper focuses on Thermoelectric materials.
Vice News Article
https://lnkd.in/gkXnEXt
Nature Paper (Tshitoyan et al 2019)
https://www.nature.com/articles/s41586-019-1335-8
Vice News Article
https://lnkd.in/gkXnEXt
Nature Paper (Tshitoyan et al 2019)
https://www.nature.com/articles/s41586-019-1335-8
lnkd.in
LinkedIn
This link will take you to a page that’s not on LinkedIn
Podcast:
Ali Ghodsi On Building A $2.7B Business And Being Considered One Of The True Founders Of Artificial Intelligence
https://alejandrocremades.com/ali-ghodsi/
Ali Ghodsi On Building A $2.7B Business And Being Considered One Of The True Founders Of Artificial Intelligence
https://alejandrocremades.com/ali-ghodsi/
Alejandro Cremades
Ali Ghodsi On Building A $28 Billion Business And Being Considered One Of The True Founders Of Artificial Intelligence - Alejandro…
Ali Ghodsi is the cofounder and CEO of Databricks which unifies analytics across data and the business. The company has raised $500M at a $2.75B valuation
The 5 graph algorithms that you should know
Rahul Agarwal describes some of the most important graph algorithms you should know and how to implement them using Python.
Rahul Agarwal describes some of the most important graph algorithms you should know and how to implement them using Python.
A simple neural network with Python and Keras
https://www.pyimagesearch.com/2016/09/26/a-simple-neural-network-with-python-and-keras
https://www.pyimagesearch.com/2016/09/26/a-simple-neural-network-with-python-and-keras
PyImageSearch
A simple neural network with Python and Keras - PyImageSearch
Learn how to create a simple neural network using the Keras neural network and deep learning library along with the Python programming language.
Detecting and treating outliers is a necessity in any dataset as it inevitably introduces the deviation in the model estimations. It can make the difference between winning and loosing a data science competition.
https://lnkd.in/fMV6GaY
This article deals with the detection of the outliers in Time Series data using different ideas, every idea improving upon the previous one and finally treating the outliers in the best way possible.
Hint of ideas covered.....
Idea #1 — Winsorization
Idea #2 Standard deviation etc.
https://lnkd.in/fMV6GaY
This article deals with the detection of the outliers in Time Series data using different ideas, every idea improving upon the previous one and finally treating the outliers in the best way possible.
Hint of ideas covered.....
Idea #1 — Winsorization
Idea #2 Standard deviation etc.
Medium
Forecasting: how to detect outliers?
(the article below is an extract from the book Data Science for Supply Chain Forecast, available here)
An article covering the case study over "Customer Transaction Prediction using LightGBM".
https://medium.com/analytics-vidhya/https-medium-com-kushagrarajtiwari-customer-transaction-prediction-3191c6c634dc
It comprehensively covers:
1. General Business Significance of this problem
2. Exploratory Data Analysis
3. Feature Engineering
4. Why use LightGBM for this problem
A good read if you want to explore problems in bank/financial domain.
https://medium.com/analytics-vidhya/https-medium-com-kushagrarajtiwari-customer-transaction-prediction-3191c6c634dc
It comprehensively covers:
1. General Business Significance of this problem
2. Exploratory Data Analysis
3. Feature Engineering
4. Why use LightGBM for this problem
A good read if you want to explore problems in bank/financial domain.
Medium
Customer Transaction Prediction using LightGBM
Exploratory Data Analysis and modelling with imbalanced data.