Professor Andrew Ng :

With NeurIPS 2018 on right now, it has been exactly 10 years since we proposed the controversial idea of using CUDA+GPUs for deep learning! h/t goodfellow_ian who in 2008 helped build our first GPU server in his Stanford dorm.

🌎 http://www.cs.cmu.edu/~dst/NIPS/nips08-workshop/


I think this story is important for all of you working in a dorm room or garage right now. We live in an age where what you do today can have a massive global impact in 10 years.

✴️ @AI_Python_EN
❇️ @AI_Python

Math is essential for machinel earning and deep learning mastery and we are fascinated by this book by Ivan Savov.

It's aptly called by Ivan and loved by our CEO Tarry Singh : " No Bullshit guide to Math and Physics"

Here is a concept map the shows math and physics concepts such as #mechanics #algebra #functions #vector

Minireference PDF of this nice book is available here:

🌎 https://lnkd.in/dTTf_58

Github code to exercises is here:

🌎 https://lnkd.in/dd7xZng

#DeepLearning #artificialintelligence

✴️ @AI_Python_EN
❇️ @AI_Python

Machine Learning from scratch!

Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran:

🌎 https://lnkd.in/er_ZNgY

#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

✴️ @AI_Python_EN
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50+ Data Structure and Algorithms Interview Questions for Programmers

🌎 Click Here


✴️ @AI_Python_EN
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This whitepaper on automatic ML says:
"Automatically transform any type of variable and automatically select and combine them for a optimal representation for learning".https://lnkd.in/eHxMPev
Creative marketing?
There's no automatic ML in the way these statements imply. And sure enough, as we dig a little further, we discover a much more sober list of 2 automated features for data preparation: automatic normalization of variables (subtract the mean, divide by the std.dev), TDM/TFIDF (text mining).

✴️ @AI_Python_EN
❇️ @AI_Python

A visual introduction to machine learning, Part II

http://bit.ly/2N0T42K

#AI #DeepLearning #MachineLearning #DataScience

✴️ @AI_Python_EN
❇️ @AI_Python

Learning an adversary allows us to efficiently identify rare failures in safety critical domains, to help ensure safe deployment of RL agents: https://arxiv.org/abs/1812.01647

This work will be presented shortly at the #NeurIPS18 SecML workshop (https://secml2018.github.io/ )

✴️ @AI_Python_EN
❇️ @AI_Python

AX: GPU- and TPU-backed #NumPy with differentiation and JIT compilation. https://github.com/google/jax

✴️ @AI_Python_EN
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Stanford CS231n- Dropout Assignment

🌎 Link Review

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The Visual Python Debugger for Jupyter Notebooks You’ve Always Wanted

🌎 http://bit.ly/2LuWdHq

#AI #DeepLearning #MachineLearning #DataScience #ML

✴️ @AI_Python_EN
❇️ @AI_Python

So you want to be a research scientist? Great post by Vincent Vanhoucke, Principal Scientist at Google and working on machine learning and robotics.


1. Research is about ill-posed questions with multiple (or no) answers

2. Your entire career will be spent working on things that don’t work

3. Your work will probably be obsolete the minute you publish it

4. With infinite freedom comes infinite responsibility

5. Much of research is paradoxically about risk management

6. You will need to retool often

7. You’ll have to subject yourself to intense scrutiny

8. Your entire career will largely be measured by one number

9. You won’t work a day in your life

🌎 https://lnkd.in/ftwSiMN

#machinelearning #artificialintelligence #robotics #deeplearning

✴️ @AI_Python_EN
❇️ @AI_Python

We introduce a Classical Japanese dataset called Kuzushiji-MNIST, a drop-in replacement for MNIST, plus 2 other datasets. In this work, we also try more interesting tasks like domain transfer from old Kanji to new Kanji.

Paper: https://lnkd.in/gw-b7Ag
Dataset: https://lnkd.in/gJ9RCHv

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

Google launches new search engine to help scientists find the datasets they need.

#DataSet

🌎 Link Review


✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
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Here is a Deep Graph Library, a Python Package For Graph Neural Networks

The MXNet team and the Amazon Web Services AI lab recently teamed up with New York University / NYU Shanghai to announce Deep Graph Library (DGL), a Python package that provides easy implementations of GNNs research

Click the link to install DGL. :: https://lnkd.in/dmXG8XZ

Web page: https://lnkd.in/dmXG8XZ
Github page: https://lnkd.in/dzG5hrT

#deeplearning #artificialintelligence #machinelearning #gnn

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

Building a text classification model with TensorFlow Hub and Estimators

🌎 http://bit.ly/2PjbMU7

#AI #MachineLearning #DeepLearning #DataScience


✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
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#deeplearning
An educational resource to help anyone learn deep reinforcement learning by OPENAI


https://github.com/openai/spinningup


✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

The 20 core Data Science projects every business should implement.

Link >> https://buff.ly/2i1jIO0

#DataScience #AI #DigitalTransformation

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🗣 @AI_Python_Arxiv
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The Future of Artificial Intelligence - The Future of Things Read more here:

https://ift.tt/2PpCVo7

#ArtificialIntelligence #AI #DataScience #MachineLearning #BigData #DeepLearning #NLP #Robots #IoT

✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

How To Run SQL queries in phpMyAdmin

https://goo.gl/QcD614


#Database #Java #javascript #programming #coding #sqlhelp #backend #mysql


✴️ @AI_Python_EN
🗣 @AI_Python_Arxiv
❇️ @AI_Python

talk at Interpretability and Robustness in Audio, Speech, and Language (IRASL) Workshop at NeurIPS2018 are now available online: "Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning"

#neurips2018 #neurips #irasl

🌎 Link



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🗣 @AI_Python_Arxiv
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