François Chollet

The complaints "Python is slow" or "Python is unsafe" seem misguided. The point of Python isn't to be fast or safe, it's to be flexible and hackable, and to interface well with everything else. It has become successful by serving as a frontend from which to call other libraries.
#Python is more of an interface than it is a development language. It's a UX.

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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..

🌎 The epistemology of Deep Learning"

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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

🌎 http://bit.ly/2U4ZSAf

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Knowing that people judge you by your books I picked out this selection for our new IKEA book shelves. Now I’m just waiting for any statistician to come visit.

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♨️ Free Self-Study Books on Mathematics, Machine Learning & Deep Learning

🔶1. Matrix Computations

✅ Free Book: Download here

🔶 2. A Probabilistic Theory of Pattern Recognition

💠 Free Book: Download here

✅3. Advanced Engineering Mathematics

💠 Free Book: Download here

✅ 4. Probability and Statistics Cookbook

Free Book: Download here

Machine Learning & Deep Learning Books

➡️ 1. An Introduction to Statistical Learning (with applications in R)

🖇 Free Book: Download here

➡️ 2. Probabilistic Programming and Bayesian Methods for Hackers

👉 Free Book: Download here

➡️3. The Elements of Statistical Learning

👉 Free Book: Download here

➡️4. Bayesian Reasoning and Machine Learning

👉 Free Book: Download here

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✔️ 5. Information Theory, Inference, and Learning Algorithms

👉 Free Book: Download here

✔️ 6. Deep Learning

🔗Free Book: Download here

📚 7. Neural Networks and Deep Learning

🔗 Free Book: Download here

📚 8. Supervised Sequence Labelling with Recurrent Neural Networks

🔗Free Book: Download here

📚 9. Reinforcement Learning: An Introduction

🔗 Free Book: Download here

#کتاب #هوش_مصنوعی #یادگیری_عمیق #یادگیری_تقویتی #آموزش #آمار #احتمال

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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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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

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.

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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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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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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 ↓
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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Using supervised Machine Learning to reach a desired solution #MachineLearning #deeplearning #ArtificialIntelligence #AI #TechNews #technology #deeptech
http://www.intelligentcio.com/eu/2019/02/26/using-supervised-machine-learning-to-reach-a-desired-solution/

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Lambda GPU computers power Deep Learning research at Apple, Microsoft, MIT, and Stanford. Learn more here: http://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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Introducing TensorFlow Datasets

By TensorFlow: https://lnkd.in/d2yEjSr

#MachineLearning #Data #Dataset #TensorFlow

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