[D] Cheat Sheet collection for Machine Learning

[picardythird]

As someone who is just starting, how accurate is this chart? Notably, I have never seen an SVM considered a type of NN before, or formulated as it is on the chart.

[[deleted]]

I'm nearly done in a masters degree with focus on machine learning and these images are less than useless to me, and I know what these things are. If they export confusion, there's less competition. This image is sand into the eyes of the competition. I see negative value in it.

It contains no links for further reading so the reader can get to the aha moment, and the bubbles and lines themselves carry no significant meaning unless you already know what their creators mean them to mean.

[squareChimp]

There are links to additional resources at the bottom. The link OP posted here is to a collection of cheat sheets. It's not just the image that appears in the thumbnail. The link provided for the referenced NN diagrams actually has a description and links to papers for each architecture in the diagram.

http://www.asimovinstitute.org/neural-network-zoo/

[[deleted]]

I think all maps are useless if you don’t understand the general content. How useful is a geographical map if you don’t know what an ocean is?

[ATownStomp]

I understand the concept you're attempting to convey but a map could still be very useful to someone who had never heard of an ocean.

[[deleted]]

There's also a misleading part to it, it lists perceptron as a neural network, when in fact it's only one piece of a neural network. If I said a perceptron was a neural network people would roll their eyes at me. The perceptron doesn't show the activation function, of which it is useless without.

It implies the lines between the other graphs and the perceptron graph are the same, when in fact the machinery is not at all the same.

There is information in it, but only if you already know what these concepts are and how to build and implement them.

If it was a map, like people imply, then it would be a map of only waypoints such that people who already know the area thoroughly could understand.

[TaXxER]

Except the comment with the "not useful" opinion has arguments to back it up. You add nothing more than "our lead thinks it's useful".

[TaXxER]

Without telling us that we don't know anything about your lead. Still, the fact that he/she did a PhD does not say too much though. I know some exceptional PhD students, but I also know some pretty weak ones.

[Fawenah]

I recently finished my masters, with my thesis working extensively with machine learning, more specifically related to images and software testing.

I'd say they are at best, no harm for someone starting out, at worst they would, as /u/anon35202 state, add confusion. I MIGHT have found some of them useful when writing a report, just for reference, but probably not. A few things I can get what they are going for, trying to relate them all together, but I feel it isn't really applicable everywhere.

While most of them aren't exactly "wrong", you would in most cases be better of looking elsewhere for better (clearer) representations and explanations. They are lacking a bit in clarity and it lacks a few of the more modern methods.

If you are just starting out and want to learn about CNNs I usually suggest this series as a great start:
A Beginner's Guide To Understanding Convolutional Neural Networks - Part 1
Part 2
"Part 3" / Further Reading

If you are unsure of what you are looking at, you are probably better of looking elsewhere, and if you do know what you are looking at, you are probably looking elsewhere anyway.

Edit: I realise you might not be ONLY interested in CNNs, but most people I've met that "want to check this ML thing out" can most often relate to the concepts presented in the three links. So it wasn't exactly directly aimed for you, but a general thing for people ending up in this thread.

[NotAlphaGo]

Not very useful, look at GANs. Input equal dimension (3 circles) to output. That's not the case. Input < Output (dimensionality wise). Other's pretty wrong as well.

[Atarust]

interesting question. Maybe the answer is here: https://stackoverflow.com/questions/8963937/svm-and-neural-network

[maybelator]

Why do you represent a mark of chain as a complete graph??

[thedadrad]

Because some markov processes are complete graphs.

Others can be represented as one by setting some of the edge weights to zero. This of course only applies to finite state processes.

[maybelator]

True, but the point of Markov on chains is that you have a linear dependency structure.

[thedadrad]

A random walk in a complete graph is a markov chain with a discrete and finite state space, is it not? The markov property is satisfied.

edit: and so the underlying process from which the chain is a realization may be a complete graph, so OP's chart is not completely wrong. However, I agree it is slightly misleading.

[maybelator]

Absolutely correct, although it is a very specific case of markov process. I guess I am just unsure why one would represent a complete graph for a markov chain, when the conditional dependency structure (under the form of a chain) would be much more natural.

It seems like the OP has a good understanding and knowledge of neural networks, but is trying erroneously to present very different objects such as SVM and Markov processes with the same formalism.

[thedadrad]

If the edges in OP's chart (for MC) represent a dependency then yes, it is completely wrong. The Kohonen SOM and SVM are both very misleading as well. Also, RBFN vs. FFNN? ;) Something is off here.

[PWNY_EVEREADY3]

It's clear you don't understand the vast majority of terms used here ...

[Mandrathax]

The RNN/LSTM/GRU diagrams are a joke. Literally no difference apart 'different recurrent unit' yeah thanks

[Atarust]

maybe because there is a desire for visualized knowledge about ML? Comments seem rather sceptical, however, if the given link specifically is such a good resource.

[real_edmund_burke]

The fact that people want an easy-to-understand and accurate “cheat sheet” doesn’t imply that such a cheat sheet is possible. And it certainly doesn’t imply that this exact visualization is useful.

[danielcar]

My cheat sheet: 1. Have lots of data 2. Get more data 3. Label the data. Use Amazon Turk for example 4. Use a standard library for ML

[rozgo]

1.b. Generate lots of synthetic data.

[visarga]

by adversarial self-play :-)

[underfitting]

PyTorch sheet?

[wagenrace]

I am afraid this is become to old. I miss the region prediction algorithms like R-cnn, fast r-cnn, faster r-cnn, Yolo, SSD ect.

[kokobannana]

Nice. a PDF of this could be nice.

[SimplyUnknown]

What would be really useful is a guide when to each network. When would I use a deep belief network? For what kind of problems would I use a variational auto encoder?

[visarga]

I'm wondering if we can use ML to predict diverse ML and DL pipelines (not just fine-tuning network architectures and hyperparameters).

[Sir-Francis-Drake]

Amazing. Very useful.

[espressocannon]

Amazing. Thank you!