I have seen a few papers that show plots of the “true posterior” for a generative model on a toy problem.

Before I did not understand how this could be computed, but now maybe I am closer. I’m sure you can help me

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Is this the way to do it? (See below)

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Definitions and setup:

The generative model is p(x|z)*p(z). The posterior is p(z|x).

A particular x is given and we want to know the distribution p(z|x) for that x,

using p(z|x) ~ p(x|z)*p(x) ,

i.e. without evaluate the Bayes denominator.

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1. Sample z from p(z),
2. evaluate p(x|z) for the given x, and multiply this by p(z) for the z that was sampled in step 1. This gives an “unnormalized” probability for this particular z.
3. Accept this new z as a sample using MCMC, e.g. metropolis-hasting.
4. Repeat 1-3, and then eventually make a kernel density plot of the resulting z sample locations.

I never even heard of Tensor2Tensor or FairSeq before last week and they are some of the most major ML libraries. I’m probably an extreme example, but now I’m curious on what other tools I am missing out on.

Besides the those two, I love adjustText for labeling huge TSNEs and UMAPs.

G’Day all,

I am trying to predict the steering angle of my autonomous car.

[[-0.5159285]] 0.0 [[-0.5189788]] -0.0004 [[-0.5273489]] -0.00035 [[-0.54270977]] -0.00019 [[-0.46451133]] -0.00018 [[-0.4513907]] -0.00018 

From the above, I have my predicted steering angle (based on an image) on the left and my ground truth angle on the right.

I am using 40,000 training samples over 1000 epochs with a 5 layer CNN and 4 layer Dense network with 1 output node (tanh). More params can be provided if need be.

When my epochs were 600 – would get the name predicted output but as positive steering angle.

Does this mean i just need to run this for longer? maybe 3000 epochs? or should i be adjust the learning rate and other parameters.

Thanks

I’ve been increasingly interested in ML, but I want to focus my time on things that will make me money (for the time being, until I can finally be kind of independent) and would love to make ML the thing I focus lots of my free time on if it does in fact have the potentially to be something I can make a profit off of. I don’t know anything about ML as it relates to industry, so I was wondering if anyone could answer this:

if an aspiring tech entrepreneur was to get deep in machine learning, would you say that’s time well spent?

Dear r/MachineLearning, I am a STEM graduate who became interested after my Master’s into making research in Machine Learning. I was promised a PhD, the opportunity to do cutting-edge research, real world applications and a “close” cooperation with industrial partners. But after having spent a few months reading and discussing with supervisors, a lot of work I am considered to do is centered around metaheuristic search and evolutionary computation. And although, I find it fascinating and there is some application to machine learning / DNNs, as well as companies like Uber and Cognizant are adopting it, I feel like it has too much of a niche quality and mainstream interest seems not to be catching-up with it. In case if there is any at all to begin with.

I thought it might be helpful to ask you guys, to get a neutral outside-of-the-box opinion.

Particularly, as over the last month I live and work in a scientific bubble and my prior background is not AI/ML or Computer Science to begin with. So anyone might just be able to claim anything to me without me having the ability to evaluate their claims or get any decent outside criticism.

I’m trying to create a machine that can play a certain dutch card game known as “Klaverjassen”. What I’m trying to do is generate randomly played games and have the algorithm learn good plays by looking at the outcome of these games. Then this improved bot will play games again, and keep learning, until it is sufficiently strong (AKA I can’t beat it anymore).

Each game is encoded as a feature/x-vector of 1’s and 0’s, with a single outcome y (which is the achieved score) between 0 and ~500. I was wondering what kind of regression algorithm would be good to learn this data. Simply using Linear regression isn’t going to be useful since there is a lot of interaction between the features, and manually adding in these interactions is infeasible since there is over 1,104 features.

I’m using MatLab, so algorithms/libraries in this language would be nice 🙂

For reference, this is what the data looks like: https://gofile.io/?c=kKs1rt (Matlab data file in zip)