rudyl313

One thing to keep in mind is that predicting a downturn in the short-term is different than predicting long-run performance over 10, 20 or 30 years. I think the website is trying to do the former and your article is talking about the latter.

"If you really want to own a stock that gives you no profits, no income from dividends, no voice in how its run, and actually no value what so ever other than the greater fool theory then go ahead."

While I see you sentiment here, I don't think it's actually correct. Non-voting common stock will receive distributions if any cash is left over after a liquidation and debt, preferred and high-ranked common stock holders are paid (i.e. there is a real claim on assets). Also, with any common stock that doesn't pay dividends, the reason to hold is the promise of dividends (and/or buybacks) when the company does not have any more avenues for investing excess income. This is true for non-voting shares as well.

I've written up my philosophy on beating the market, which is a little less conservative with respect to believing that markets are efficient and investing in the indices is the only prudent way to invest:

https://docs.google.com/document/d/1KXfTFYfmhb9Cy5NE0uRuf8Sv...

Using ReLU units is a newer advancement and I agree that changing the activation function does change the cost function. However, before Hinton got all excited about ReLU units, he was still showing huge improvements just by using pretraining and later by using dropout, which shouldn't change the cost function.

But the tricks/advancements I mentioned are not changing the function. They changed the initial weights and how the cost function was explored.

If these claims are true (specifically, that every local minimum is a global minimum), then why did the earlier neural networks have poor performance? Why did we need advancements like pretraining via stacked RBMs and dropout in order to make deep learning converge on usable/better models?

The market movement can be explained somewhat by the fed's rate hike expectations changing (worsening economic conditions means that the fed is more cautious about raising rates, so discount rates are lower and valuation models are higher). It's also about what the market expected earnings to be. Yes, the tech earnings are beating up the NASDAQ, but people actually expected worse from the financial sector given the very low rates.

Unfortunately this talk is kind of dated already. Most people don't stack RBMs or autoencoders to pretrain the weights anymore. If you use dropout with rectified linear units, you don't have to pretrain, even for large architectures.

A placebo won't work if you know it's a placebo.

Very cool! Thanks for sharing that