Yann LeCun
Video of my talk at the Institute of Advanced Studies workshop "Deep Learning: Alchemy or Science?", organized by Sanjeev Arora Friday Feb 22, 2019. The audience was very diverse, so I focused on the early history and dynamics of ideas in neural..
š The epistemology of Deep Learning"
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Video of my talk at the Institute of Advanced Studies workshop "Deep Learning: Alchemy or Science?", organized by Sanjeev Arora Friday Feb 22, 2019. The audience was very diverse, so I focused on the early history and dynamics of ideas in neural..
š The epistemology of Deep Learning"
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š£ @AI_Python_arXiv
Google Doubles Down On Spammers With #TensorFlow. #BigData #Analytics #MachineLearning #DataScience #AI #NLProc #IoT #IIoT #PyTorch #Python #RStats #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #TransferLearning
š https://bit.ly/2U4ZSAf
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š https://bit.ly/2U4ZSAf
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š£ @AI_Python_arXiv
Knowing that people judge you by your books I picked out this selection for our new IKEA book shelves. Now Iām just waiting for any statistician to come visit.
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Forwarded from DLeX: AI Python (Farzadš¦
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āØļø Free Self-Study Books on Mathematics, Machine Learning & Deep Learning
š¶1. Matrix Computations
ā Free Book: Download here
š¶ 2. A Probabilistic Theory of Pattern Recognition
š Free Book: Download here
ā 3. Advanced Engineering Mathematics
š Free Book: Download here
ā 4. Probability and Statistics Cookbook
Free Book: Download here
Machine Learning & Deep Learning Books
ā”ļø 1. An Introduction to Statistical Learning (with applications in R)
š Free Book: Download here
ā”ļø 2. Probabilistic Programming and Bayesian Methods for Hackers
š Free Book: Download here
ā”ļø3. The Elements of Statistical Learning
š Free Book: Download here
ā”ļø4. Bayesian Reasoning and Machine Learning
š Free Book: Download here
āļø@AI_Python
āļø@Data_Experts
āļø 5. Information Theory, Inference, and Learning Algorithms
š Free Book: Download here
āļø 6. Deep Learning
šFree Book: Download here
š 7. Neural Networks and Deep Learning
š Free Book: Download here
š 8. Supervised Sequence Labelling with Recurrent Neural Networks
šFree Book: Download here
š 9. Reinforcement Learning: An Introduction
š Free Book: Download here
#Ś©ŲŖŲ§ŲØ #ŁŁŲ“_Ł ŲµŁŁŲ¹Ū #ŪŲ§ŲÆŚÆŪŲ±Ū_Ų¹Ł ŪŁ #ŪŲ§ŲÆŚÆŪŲ±Ū_ŲŖŁŁŪŲŖŪ #Ų¢Ł ŁŲ²Ų“ #Ų¢Ł Ų§Ų± #Ų§ŲŲŖŁ Ų§Ł
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āļø@Data_Experts
š¶1. Matrix Computations
ā Free Book: Download here
š¶ 2. A Probabilistic Theory of Pattern Recognition
š Free Book: Download here
ā 3. Advanced Engineering Mathematics
š Free Book: Download here
ā 4. Probability and Statistics Cookbook
Free Book: Download here
Machine Learning & Deep Learning Books
ā”ļø 1. An Introduction to Statistical Learning (with applications in R)
š Free Book: Download here
ā”ļø 2. Probabilistic Programming and Bayesian Methods for Hackers
š Free Book: Download here
ā”ļø3. The Elements of Statistical Learning
š Free Book: Download here
ā”ļø4. Bayesian Reasoning and Machine Learning
š Free Book: Download here
āļø@AI_Python
āļø@Data_Experts
āļø 5. Information Theory, Inference, and Learning Algorithms
š Free Book: Download here
āļø 6. Deep Learning
šFree Book: Download here
š 7. Neural Networks and Deep Learning
š Free Book: Download here
š 8. Supervised Sequence Labelling with Recurrent Neural Networks
šFree Book: Download here
š 9. Reinforcement Learning: An Introduction
š Free Book: Download here
#Ś©ŲŖŲ§ŲØ #ŁŁŲ“_Ł ŲµŁŁŲ¹Ū #ŪŲ§ŲÆŚÆŪŲ±Ū_Ų¹Ł ŪŁ #ŪŲ§ŲÆŚÆŪŲ±Ū_ŲŖŁŁŪŲŖŪ #Ų¢Ł ŁŲ²Ų“ #Ų¢Ł Ų§Ų± #Ų§ŲŲŖŁ Ų§Ł
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CS294-158 Deep Unsupervised Learning Spring 2019
https://sites.google.com/view/berkeley-cs294-158-sp19/home
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š£ @AI_Python_arXiv
https://sites.google.com/view/berkeley-cs294-158-sp19/home
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š£ @AI_Python_arXiv
How Machine Learning and AI are Changing eSports and Knowledge Itself
#Automation #AI #MachineLearning https://medium.com/swlh/how-machine-learning-and-ai-are-changing-esports-and-knowledge-itself-b4d977473cc1?source=rss-------8-----------------artificial_intelligence
#Automation #AI #MachineLearning https://medium.com/swlh/how-machine-learning-and-ai-are-changing-esports-and-knowledge-itself-b4d977473cc1?source=rss-------8-----------------artificial_intelligence
Medium
How Machine Learning and AI are Changing eSports and Knowledge Itself
AI, Machine Learning, and eSports are all burgeoning industries. Hereās how theyāre changing things.
I may be wrong, but I get the impression that some data science people believe regression comes in just two flavors - OLS linear and binary logistic.
Setting aside the relationship between neural nets and regression, and that VAR, GARCH, Structural Equation Models and numerous other statistical models are really forms of regression, I have no idea how many kinds of "regression" are in common use.
"Dozens" would probably be an underestimate. There are countless other types which are used infrequently but essential in certain circumstances, like a fifth pitch in baseball.
Moreover, there is usually more than one way to estimate most statistical models. It's not unusual for a statistician to run one kind of regression model several ways with maximum likelihood estimation (MLE) and Bayesian alternatives, for example.
We have a BIG regression decision tree, and our choices are seldom inconsequential.
ā“ļø @AI_Python_EN
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š£ @AI_Python_arXiv
Setting aside the relationship between neural nets and regression, and that VAR, GARCH, Structural Equation Models and numerous other statistical models are really forms of regression, I have no idea how many kinds of "regression" are in common use.
"Dozens" would probably be an underestimate. There are countless other types which are used infrequently but essential in certain circumstances, like a fifth pitch in baseball.
Moreover, there is usually more than one way to estimate most statistical models. It's not unusual for a statistician to run one kind of regression model several ways with maximum likelihood estimation (MLE) and Bayesian alternatives, for example.
We have a BIG regression decision tree, and our choices are seldom inconsequential.
ā“ļø @AI_Python_EN
āļø @AI_Python
š£ @AI_Python_arXiv
image_2019-02-26_17-58-34.png
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You know what a neural network is, and you know what a ML project workflow looks like. Now how do you implement it throughout your entire company? Week 3 of AI for Everyone will walk you through it:
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š£ @AI_Python_arXiv
image_2019-02-26_22-02-43.png
789.8 KB
Left: how you would plot the Xception architecture in a paper.
Right: how you would implement it with the Functional API (that's the entire code).
1:1 match between how you think about it and how you write it.
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Right: how you would implement it with the Functional API (that's the entire code).
1:1 match between how you think about it and how you write it.
ā“ļø @AI_Python_EN
āļø @AI_Python
š£ @AI_Python_arXiv
How does Google Translate's AI work? https://youtu.be/sIoHFPGOY0I
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Lingvo is a deep learning framework used for sequence modeling tasks like machine translation, speech recognition, and speech synthesis. Learn more here ā
https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb?linkId=63952201
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https://medium.com/tensorflow/lingvo-a-tensorflow-framework-for-sequence-modeling-8b1d6ffba5bb?linkId=63952201
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There are links to lots of AI ethics resources & articles in this post: "In Favor of Developing Ethical Best Practices in AI Research"
https://ai.stanford.edu/blog/ethical_best_practices/
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https://ai.stanford.edu/blog/ethical_best_practices/
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Using supervised Machine Learning to reach a desired solution #MachineLearning #deeplearning #ArtificialIntelligence #AI #TechNews #technology #deeptech
https://www.intelligentcio.com/eu/2019/02/26/using-supervised-machine-learning-to-reach-a-desired-solution/
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š£ @AI_Python_arXiv
https://www.intelligentcio.com/eu/2019/02/26/using-supervised-machine-learning-to-reach-a-desired-solution/
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š£ @AI_Python_arXiv
Lambda GPU computers power Deep Learning research at Apple, Microsoft, MIT, and Stanford. Learn more here: https://LAMBDALABS.COM
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Introduction to Deep Learning
Slides, course materials, demos, and implementations
https://chokkan.github.io/deeplearning/
ISSCC2018 - 50 Years of Computer Architecture:From Mainframe CPUs to Neural-Network TPUs
https://www.youtube.com/watch?v=NZS2TtWcutc
Theorizing from Data by Peter Norvig (Video Lecture)
https://catonmat.net/theorizing-from-data-by-peter-norvig-video-lecture
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š£ @AI_Python_arXiv
Slides, course materials, demos, and implementations
https://chokkan.github.io/deeplearning/
ISSCC2018 - 50 Years of Computer Architecture:From Mainframe CPUs to Neural-Network TPUs
https://www.youtube.com/watch?v=NZS2TtWcutc
Theorizing from Data by Peter Norvig (Video Lecture)
https://catonmat.net/theorizing-from-data-by-peter-norvig-video-lecture
ā“ļø @AI_Python_EN
āļø @AI_Python
š£ @AI_Python_arXiv
Introducing TensorFlow Datasets
By TensorFlow: https://lnkd.in/d2yEjSr
#MachineLearning #Data #Dataset #TensorFlow
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By TensorFlow: https://lnkd.in/d2yEjSr
#MachineLearning #Data #Dataset #TensorFlow
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6 Tips to Improve Your Code for Data Science (with links)
1. Strictly follow style standards
-> https://lnkd.in/gKZUjVa
2. Use a linter to enforce style standards
-> https://lnkd.in/d_prybR
3. Write modular, generic, object-oriented code -
-> https://lnkd.in/gsynW6Q
-> https://lnkd.in/dx53u53
4. Write unit tests to test your functions and methods
-> https://lnkd.in/dsy-bPu
5. Organize your code base
-> https://lnkd.in/dviGffH
6. Separate exploration and production development, and develop production code using test-driven development (TDD)
-> https://lnkd.in/dMn-s32
If you'd like some real code examples, I've got 5 end-to-end data science projects with instructions, data, code, and complete video walkthroughs as part of the DSDJ course.
These examples and videos will walk you through everything that you need to take your data science coding skills to the next level.
To learn more, join our mail list today at https://lnkd.in/g7AYg72
#datascience #programming
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š£ @AI_Python_arXiv
1. Strictly follow style standards
-> https://lnkd.in/gKZUjVa
2. Use a linter to enforce style standards
-> https://lnkd.in/d_prybR
3. Write modular, generic, object-oriented code -
-> https://lnkd.in/gsynW6Q
-> https://lnkd.in/dx53u53
4. Write unit tests to test your functions and methods
-> https://lnkd.in/dsy-bPu
5. Organize your code base
-> https://lnkd.in/dviGffH
6. Separate exploration and production development, and develop production code using test-driven development (TDD)
-> https://lnkd.in/dMn-s32
If you'd like some real code examples, I've got 5 end-to-end data science projects with instructions, data, code, and complete video walkthroughs as part of the DSDJ course.
These examples and videos will walk you through everything that you need to take your data science coding skills to the next level.
To learn more, join our mail list today at https://lnkd.in/g7AYg72
#datascience #programming
ā“ļø @AI_Python_EN
āļø @AI_Python
š£ @AI_Python_arXiv