Andrej Karpathy’s character level RNN model - is a masterpiece! It is a sufficiently trained model using text generators that gives some eye-popping results. This article by Pranjal Srivastava introduces us to #TextGenerators and their applications in creating a Machine Learning model that can write sonnets just like a Poet !
https://bit.ly/2u7kigq
✴️ @AI_Python_EN
https://bit.ly/2u7kigq
✴️ @AI_Python_EN
HOW TO IMPROVE YOUR SKILL ON TEXT DATA?
Rubens Zimbres, PhD compile amazing resources on Machine Learning, NLP, and Computer Vision. On NLP Side he cover pretty much every common topic on NLP, this is very useful because as data scientist we often dealing with text data.
Yo can see the repository here https://lnkd.in/fyyvZYt
#repository #machinelearning #patternrecognition #artificialintellegence
✴️ @AI_Python_EN
Rubens Zimbres, PhD compile amazing resources on Machine Learning, NLP, and Computer Vision. On NLP Side he cover pretty much every common topic on NLP, this is very useful because as data scientist we often dealing with text data.
Yo can see the repository here https://lnkd.in/fyyvZYt
#repository #machinelearning #patternrecognition #artificialintellegence
✴️ @AI_Python_EN
Predicting how the stock market will perform is one of the most difficult things to do given the sheer number of factors involved. Can #machinelearning & #deeplearning make a difference? Here are 3 articles to get you started:
1. Stock Prices Prediction Using #ML and Deep Learning Techniques (with #Python codes) - https://bit.ly/2TKqABa
2. Comparative Stock Market Analysis in R using Quandl & tidyverse (Part 1) - https://bit.ly/2u5HlbG
3. Bollinger Bands and their use in Stock Market Analysis (using Quandl & tidyverse in R - Part 2) - https://bit.ly/2JaiFct
✴️ @AI_Python_EN
1. Stock Prices Prediction Using #ML and Deep Learning Techniques (with #Python codes) - https://bit.ly/2TKqABa
2. Comparative Stock Market Analysis in R using Quandl & tidyverse (Part 1) - https://bit.ly/2u5HlbG
3. Bollinger Bands and their use in Stock Market Analysis (using Quandl & tidyverse in R - Part 2) - https://bit.ly/2JaiFct
✴️ @AI_Python_EN
Glad to see that our #GAN research works enable people to "generate realistic dance videos of NBA players for in-game entertainment." #pix2pixHD, #vid2vid https://medium.com/@getxpire/how-we-used-ai-to-make-nba-players-dance-2fdbe6c63a97
✴️ @AI_Python_EN
✴️ @AI_Python_EN
Whoa: "Run VS Code on a remote server" https://github.com/codercom/code-server … I haven't been able to fight Emacs+screen+mosh addiction for ~20 years now (well, mosh was recent addition) because I loved being able to move between machines & have my state preserved. I've been waiting for this!
✴️ @AI_Python_EN
✴️ @AI_Python_EN
In analytics, data typically cannot be used as is, even when "structured." Statisticians and data scientists often spend considerable time on data preparation.
Categorical data, for instance, often needs to be recoded. For example, we may need collapse it into a smaller number of categories for modeling purposes. The new variable must be interpretable as well as useful in the modeling.
Variables are often combined into new ones. This can be done judgmentally (with care) or based on exploratory data analysis (EDA).
Logarithmic and other transformations of data are also frequently necessary for modeling purposes.
Missing data are nearly always a concern, and how they are handled is seldom inconsequential. Some academic statisticians have spent significant parts of their careers on this one topic.
All the foregoing examples require subject matter knowledge and background pertinent to the project. If the project is repeated, then the learnings and code can be leveraged to reduce or even automate data preparation, but the first time around it can consume the majority of our time.
The upside is that we can learn a lot from EDA, as an archeologist can from the location of artifacts and their proximity to others.
✴️ @AI_Python_EN
Categorical data, for instance, often needs to be recoded. For example, we may need collapse it into a smaller number of categories for modeling purposes. The new variable must be interpretable as well as useful in the modeling.
Variables are often combined into new ones. This can be done judgmentally (with care) or based on exploratory data analysis (EDA).
Logarithmic and other transformations of data are also frequently necessary for modeling purposes.
Missing data are nearly always a concern, and how they are handled is seldom inconsequential. Some academic statisticians have spent significant parts of their careers on this one topic.
All the foregoing examples require subject matter knowledge and background pertinent to the project. If the project is repeated, then the learnings and code can be leveraged to reduce or even automate data preparation, but the first time around it can consume the majority of our time.
The upside is that we can learn a lot from EDA, as an archeologist can from the location of artifacts and their proximity to others.
✴️ @AI_Python_EN
What skills should I study to moving in the Fourth Industrial Revolution?
Five years from now, over one-third of skills (35%) that are considered important in today’s workforce will have changed.
By 2020, the Fourth Industrial Revolution will have brought us advanced robotics and autonomous transport, artificial intelligence and machine learning, advanced materials, biotechnology and genomics.
These developments will transform the way we live, and the way we work. Some jobs will disappear, others will grow and jobs that don’t even exist today will become commonplace. What is certain is that the future workforce will need to align its skillset to keep pace.
https://lnkd.in/gBxcrnm
#AI
✴️ @AI_Python_EN
Five years from now, over one-third of skills (35%) that are considered important in today’s workforce will have changed.
By 2020, the Fourth Industrial Revolution will have brought us advanced robotics and autonomous transport, artificial intelligence and machine learning, advanced materials, biotechnology and genomics.
These developments will transform the way we live, and the way we work. Some jobs will disappear, others will grow and jobs that don’t even exist today will become commonplace. What is certain is that the future workforce will need to align its skillset to keep pace.
https://lnkd.in/gBxcrnm
#AI
✴️ @AI_Python_EN
We study bias-variance dilemma in class, but reading the paper gives important historical perspective. You'll see that many of the 'unreasonable effectiveness' and 'surprising findings' are predicted in this paper. Thread..(1) http://www.dam.brown.edu/people/geman/Homepage/Essays%20and%20ideas%20about%20neurobiology/bias-variance.pdf
✴️ @AI_Python_EN
✴️ @AI_Python_EN
“If you only read the books that everyone else is reading, you can only think what everyone else is thinking.”
Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other medium of learning.
There are hundreds of books out there about data science. How do you choose where to start? Which books are ideal for learning a certain technique or domain? While there’s no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books we’ll see shortly.
I have divided the books into different domains to make things easier for you:
1. Books on Statistics
2. Books on Probability
3. Books on Machine Learning
4. Books on Deep Learning
5. Books on Natural Language Processing (NLP)
6. Books on Computer Vision
7. Books on Artificial Intelligence
8. Books on Tools/Languages
- Python
- R
Link : https://bit.ly/2IOPV8T
#python #books #artificialintelligence #datascience
#machinelearning #statistics #datascientist #deeplearning
✴️ @AI_Python_EN
Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world of knowledge condensed into a few hundred pages. There is a magic and allure to books that I have never found in any other medium of learning.
There are hundreds of books out there about data science. How do you choose where to start? Which books are ideal for learning a certain technique or domain? While there’s no one-shoe-fits-all answer to this, I have done my best to cut down the list to these 27 books we’ll see shortly.
I have divided the books into different domains to make things easier for you:
1. Books on Statistics
2. Books on Probability
3. Books on Machine Learning
4. Books on Deep Learning
5. Books on Natural Language Processing (NLP)
6. Books on Computer Vision
7. Books on Artificial Intelligence
8. Books on Tools/Languages
- Python
- R
Link : https://bit.ly/2IOPV8T
#python #books #artificialintelligence #datascience
#machinelearning #statistics #datascientist #deeplearning
✴️ @AI_Python_EN
AI, Python, Cognitive Neuroscience
“If you only read the books that everyone else is reading, you can only think what everyone else is thinking.” Every person has their own way of learning. What helped me break into data science was books. There is nothing like opening your mind to a world…
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As a #datascience professional, you are bound to come across applications and problems to be solved through #LinearProgramming. Better get started today with these two awesome tutorials:
Introductory guide on Linear Programming for (aspiring) #datascientists - https://lnkd.in/fWcqKMn
A Beginner’s guide to Shelf Space Optimization using Linear Programming - https://lnkd.in/f8swcdR
✴️ @AI_Python_EN
Introductory guide on Linear Programming for (aspiring) #datascientists - https://lnkd.in/fWcqKMn
A Beginner’s guide to Shelf Space Optimization using Linear Programming - https://lnkd.in/f8swcdR
✴️ @AI_Python_EN
"Best Practices of ML Engineering"
https://lnkd.in/d-g49zg
#ArtificialIntelligence #DeepLearning #MachineLearning
✴️ @AI_Python_EN
https://lnkd.in/d-g49zg
#ArtificialIntelligence #DeepLearning #MachineLearning
✴️ @AI_Python_EN
Everyone knows how important hand-labeled data is for machine learning and also how tedious it can be to collect large labeled data. This usually involves a lot of time and expertise. We actually know it because at idealo we have a lot of supervised machine learning projects where good labeled data is key to our models. So having any solution that can generate good labeled data quickly is beneficial.
Google AI now collaborated with Stanford and Brown University to explore a potential solution to this problem where they harness organizational knowledge to quickly label large training datasets. Their framework is called Snorkel DryBell which is based on the open-source Snorkel (Link: https://lnkd.in/dbspu-z) system. The idea is pretty simple instead of having humans labeling your data, Snorkel DryBell enables writing labeling functions that label training data programmatically. This way you can generated labeled data much faster but obviously it's also of much lower quality. It's a very interesting read! Check it out! #deeplearning #machinelearning
Article: https://lnkd.in/ddsKTsF
Paper: https://lnkd.in/d-cRdSz
✴️ @AI_Python_EN
Google AI now collaborated with Stanford and Brown University to explore a potential solution to this problem where they harness organizational knowledge to quickly label large training datasets. Their framework is called Snorkel DryBell which is based on the open-source Snorkel (Link: https://lnkd.in/dbspu-z) system. The idea is pretty simple instead of having humans labeling your data, Snorkel DryBell enables writing labeling functions that label training data programmatically. This way you can generated labeled data much faster but obviously it's also of much lower quality. It's a very interesting read! Check it out! #deeplearning #machinelearning
Article: https://lnkd.in/ddsKTsF
Paper: https://lnkd.in/d-cRdSz
✴️ @AI_Python_EN
Google AI Blog
Harnessing Organizational Knowledge for Machine Learning
Posted by Alex Ratner, Stanford University and Cassandra Xia, Google AI One of the biggest bottlenecks in developing machine learning (M...
The Matrix Calculus You Need For Deep Learning
By Terence Parr and Jeremy Howard : https://lnkd.in/dC5MqZM
#100DaysOfMLCode #ArtificialIntelligence #BigData #DeepLearning #MachineLearning #NeuralNetworks
✴️ @AI_Python_EN
By Terence Parr and Jeremy Howard : https://lnkd.in/dC5MqZM
#100DaysOfMLCode #ArtificialIntelligence #BigData #DeepLearning #MachineLearning #NeuralNetworks
✴️ @AI_Python_EN
Here's a list of ALL the #machinelearning and #deeplearning articles we have published in March so far. This is for both beginners and advanced #datascience enthusiasts!
• 11 Steps to Transition into #DataScience (for Reporting / MIS / BI Professionals) - https://buff.ly/2EPTCqB
• 5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic) - https://buff.ly/2TS3SXQ
• Top 5 Data Science GitHub Repositories and Reddit Discussions (February 2019) - https://buff.ly/2GYFvBW
• Hands-On Introduction to creditR: An Amazing R Package to Enhance Credit Risk Scoring and Validation - https://buff.ly/2CiGWa3
• A Step-by-Step #NLP Guide to Learn ELMo for Extracting Features from Text - https://buff.ly/2HCQjFo
• The Path to Artificial General Intelligence with Professor Melanie Mitchell - https://buff.ly/2HmDrnj
✴️ @AI_Python_EN
• 11 Steps to Transition into #DataScience (for Reporting / MIS / BI Professionals) - https://buff.ly/2EPTCqB
• 5 Amazing Deep Learning Frameworks Every Data Scientist Must Know! (with Illustrated Infographic) - https://buff.ly/2TS3SXQ
• Top 5 Data Science GitHub Repositories and Reddit Discussions (February 2019) - https://buff.ly/2GYFvBW
• Hands-On Introduction to creditR: An Amazing R Package to Enhance Credit Risk Scoring and Validation - https://buff.ly/2CiGWa3
• A Step-by-Step #NLP Guide to Learn ELMo for Extracting Features from Text - https://buff.ly/2HCQjFo
• The Path to Artificial General Intelligence with Professor Melanie Mitchell - https://buff.ly/2HmDrnj
✴️ @AI_Python_EN
⚠️ The most cliché things that some data scientists actually say and do
On deep learning:
✔️ Say: «Deep learning is overkill in most cases.»
❌ Do: Use deep learning for everything.
On Excel:
✔️ Say: «Excel is such a useless and outdated tool.»
❌ Do: Use Excel every day.
On statistics:
✔️ Say: «My biggest strength is math and statistics.»
❌ Do: Google the definition of standard deviation.
On correlation:
✔️ Say: «Correlation is not causation.»
❌ Do: Base feature selection entirely on correlation.
On big data
✔️ Say: «We should use a big data store for this.»
❌ Do: Put everything in an SQL database.
On careers
✔️ Say: «I truly believe in the mission of this company. We’re going to change the world.»
❌ Do: Change jobs every year or whenever someone offers slightly more money.
✔️ Say: «I’m a senior data scientist and machine learning expert.»
❌ Do: Still, haven’t shipped a model to production.
On science
✔️ Say: «We use the scientific method. Every hypothesis needs to be tested.»
❌ Do: Deploy a model straight to production because it converged on the training set.
On academic papers
✔️ Say: «I read a lot of papers.»
❌ Do: Read the abstract of a DeepMind paper once.
On p-values
✔️ Say: «The p-value is very often misunderstood.»
❌ Do: Offer a flawed explanation of p-values.
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
On deep learning:
✔️ Say: «Deep learning is overkill in most cases.»
❌ Do: Use deep learning for everything.
On Excel:
✔️ Say: «Excel is such a useless and outdated tool.»
❌ Do: Use Excel every day.
On statistics:
✔️ Say: «My biggest strength is math and statistics.»
❌ Do: Google the definition of standard deviation.
On correlation:
✔️ Say: «Correlation is not causation.»
❌ Do: Base feature selection entirely on correlation.
On big data
✔️ Say: «We should use a big data store for this.»
❌ Do: Put everything in an SQL database.
On careers
✔️ Say: «I truly believe in the mission of this company. We’re going to change the world.»
❌ Do: Change jobs every year or whenever someone offers slightly more money.
✔️ Say: «I’m a senior data scientist and machine learning expert.»
❌ Do: Still, haven’t shipped a model to production.
On science
✔️ Say: «We use the scientific method. Every hypothesis needs to be tested.»
❌ Do: Deploy a model straight to production because it converged on the training set.
On academic papers
✔️ Say: «I read a lot of papers.»
❌ Do: Read the abstract of a DeepMind paper once.
On p-values
✔️ Say: «The p-value is very often misunderstood.»
❌ Do: Offer a flawed explanation of p-values.
❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
New documentation about how differentiable programming works in Swift: • Differentiable Functions and Differentiation APIs: https://github.com/tensorflow/swift/blob/master/docs/DifferentiableFunctions.md … • Differentiable Types: https://github.com/tensorflow/swift/blob/master/docs/DifferentiableTypes.md … With language integration, autodiff is just a compiler implementation detail.
✴️ @AI_Python_EN
✴️ @AI_Python_EN
Machine Learning with Python, Jupyter, KSQL and TensorFlow https://ift.tt/2FbgQq6 #python #tensorflow #jupyter #ksql https://ift.tt/2TFCqNC
✴️ @AI_Python_EN
✴️ @AI_Python_EN
Discover Computer Vision Datasets with this search engine
#dataset #image #visual #search #engine #vision
https://www.visualdata.io
✴️ @AI_Python_EN
#dataset #image #visual #search #engine #vision
https://www.visualdata.io
✴️ @AI_Python_EN
MIT DeepLearning Basics — Introduction and Overview with TensorFlow #robotics #game #games
bit.ly/2E4xnx6
✴️ @AI_Python_EN
bit.ly/2E4xnx6
✴️ @AI_Python_EN