But Geoff Hinton...
785 karma · joined May 14, 2017
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E: alex [@@@] numberboost.com
L: linkedin.com/in/alxcnwy/
X: @alxcnwy
Consulting: www.numberboost.com
Here is a vid of one my talks on deep learning for computer vision: https://www.youtube.com/watch?v=X4Q6C915sUY
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https://www.amazon.com/Quantum-Physics-Dummies-Steven-Holzne...
Strictly speaking, you should be incorporating level 2 order book data to properly gauge fees and simulate real world profits from your backtests.
I also really like the idea of the fund crowdsourcing many predictions with the obvious but unsaid endgame being the meta-model much like how the random forest algorithm ensambles many weak predictors into a strong predictor that's far more accurate than any single weak predictor.
Interested to see where this is in a couple of years...
- Talking Machines
- The O'Reilly Data Show
- Software Engineering Radio
- Software Engineering Daily
- AWS Re: Invent series
- Econtalk
- Conversations with Tyler
- Waking Up with Sam HarrisIf you count all that stuff then the average person is probably working more than 8 hours too.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.466...
My advice would be to spend at least a month in each place - it's also a discontinuity point in accommodation pricing. AirBnB snaps into monthly discounts and it's the minimum period for short term rental. Definitely just google "city name short term rental" and look at the local sites - you can find some amazing apartments for a month at a time.
I'm traveling with my girlfriend who works in online marketing. I believe it makes it a lot easier but we've been together the entire time so I can't make a fair comparison. I can see how it might get lonely but I really enjoy hanging out with the girlfriend and go to meetups while traveling so haven't felt lonely at all...
I'd imagine it's sufficiently derivative as to be allowed? What if you wrote a model that produced summaries of the book - would that be allowed?
The go-to graduate level machine learning text "The Elements of Statistical Learning" was written by 2 statistics professors (one, Prof. Hastie, a fellow South African! :)
Granted, neural networks are often taught as a bolt-on lecture in statistics course machine learning modules but topics like regularization and a rigorous study of overfitting were born in stats departments.
Would you mind please elaborating a little more on how you're thinking about theme-specific sentiment (for the NLP students here :) - is it along the lines of Socher's aspect-specific sentiment or something else?
I just wish there was something like this with code examples. It's so frustrating when papers say things like 'we omit biases' and so on because it makes it difficult to reproduce their implementation arghghg.
Then for overall curriculum, I'd suggest:
1. start with basic machine learning (not neural networks) and in particular, read through the scikit-learn docs and watch a few tutorials on youtube. spend some time getting familiar with jupyter notebooks and pandas and tackle some real-world problems (kaggle is great or google around for datasets that excite you). Make sure you can solve regression, classification and clustering problems and understand how to measure the accuracy of your solution (understand things like precision, recall, mse, overfitting, train/test/validation splits)
2. Once you're comfortable with traditional machine learning, get stuck into neural networks by doing the fast.ai course. It's seriously good and will give you confidence in building near cutting-edge solutions to problems
3. Pick a specific problem area and watch a stanford course on it (e.g. cs231n for computer vision or cs224n for NLP)
4. Start reading papers. I recommend Mendeley to keep notes and organize them. The stanford courses will mention papers. Read those papers and the papers they cite.
5. Start trying out your own ideas and implementations.
While you do the above, supplement with:
* Talking Machines and O'Reilly Data Show podcasts
* Follow people like Richard Socher, Andrej Karpathy and other top researchers on Twitter
Good luck and enjoy!