Turing Finance @TuringFinance
Where Computer Science meets Finance. TuringFinance.com Cape Town, South Africa Joined April 2014-
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AI solves problems by finding patterns in data. AI can only play chess because patterns emerge from the board, pieces, and rules staying the same game after game. Markets are different. So how should investors who want to add value using AI go about it? A THREAD 👇🏼
Learning to Reason with Neural Module Networks bair.berkeley.edu/blog/2017/06/2…
A really interesting article about a new math that might answer some of the most interesting questions of our time.
A really interesting article about a new math that might answer some of the most interesting questions of our time.
Can #machinelearning automate the design of neural networks? Learn more about an approach we call "AutoML" goo.gl/gclgdf
Great post, Kullback-Leibler Divergence Explained: countbayesie.com/blog/2017/5/9/…. #MachineLearning #Statistics
LOL! Brilliant, but only 1/3 true. What I also do: cry about (2) how messy historical data is and (3) how long hyperparameter tuning takes!
LOL! Brilliant, but only 1/3 true. What I also do: cry about (2) how messy historical data is and (3) how long hyperparameter tuning takes!
Very few could weave the best of Fama, Shiller, and Darwin together as coherently as Prof Lo does: bloomberg.com/news/audio/201…. H/t @Ritholtz
This is mind blowing! Unsupervised Sentiment Neuron from @OpenAI -google.co.za/amp/s/blog.ope… #MachineLearning #NeuralNetworks
As @TensorFlow's #java API matures and smartphones improve, apps will learn from and adapt to their surroundings research.googleblog.com/2017/04/federa… :-D
Mind-blowing essays like this - written by leading minds to demystify the cutting edge of science - is why IAS ideas is my favourite site 🖖
Mind-blowing essays like this - written by leading minds to demystify the cutting edge of science - is why IAS ideas is my favourite site 🖖
Some neat visualizations which explain the intuition behind statistical concepts students.brown.edu/seeing-theory/ (! mobile friendly) #DataScience
Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data - twiecki.github.io/blog/2017/03/1…. Very neat @twiecki! Time to learn PyMC3 :-D
Stunning! Visualizations like these which communicate the intuition behind "complex" algorithms are essential for business & user adoption.
Stunning! Visualizations like these which communicate the intuition behind "complex" algorithms are essential for business & user adoption.
Meta-models in scikit-learn are awesome and the docs are better than most University textbooks. Kudos @scikit_learn #MachineLearning
If you're interested in Google's approach to business and their commitment to #MachineLearning you should read this: audible.co.uk/pd?asin=B004VF…
Here’s the complete #TFDevSummit playlist for those who want to rewatch a talk, or if you missed the livestream. goo.gl/xb561f
This perl: print "prime" if(1x$a)!~/^1?$|^(11+?)\1+$/; from github.com/MinhasKamal/Cr… is too cool! q(❂‿❂)p
Total Recall; not a typical "Quant book" but inspiring and filled with lessons nonetheless :-) audible.co.uk/pd?asin=B009GL… h/t @Schwarzenegger
Impressive! They use #NeuralNetworks to predict the params of a Weibull dist describing P(churn), not churn events: ragulpr.github.io/2016/12/22/WTT…
Dreaming of Thinking Machines; The Dream, The Brain, The Machine. Real though provocation by my friend @QuantsPortal quantsportal.com/dreaming-think…
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