Hi, I am interesting in ML, I want to try create some model for stock trading, I would like to focus on placed orders and patterns how price reacts on it. On which algorithm or category of algorithms I should focus?

Cheers

I’ll start – sparse matrices in Pandas that aren’t supported in SKLearn. Both tools are great but for some reason it took hours to find out that SKLearn will by default inflate the sparse matrix (in my case 30MB>>20GB) without throwing any warning…

Are there any good RESNET results on MNIST? I know it is a relatively easy problem. But I have tried many different configurations and it generalizes worse than a simple network which has 2-Conv layers+Dense (99.63).

I have tried reducing the number of filters to 4. (99.20~99.40)

I have tried different optimizers. (SGD,ADAM)

Any suggestions, any links?

https://soundcloud.com/theforevernow/ai-xmas-rudolph-the-red-nosed-king-of-all-earth

The lyrics were created by Janelle Shane and sourced from this post: https://twitter.com/JanelleCShane/status/1209485377154801665

Rudolph the red nosed
Rudolph the red nosed reindeer
with it’s red belly
The all gracious king
the all gracious king
of all the Earth

On Christmas Day a true and holy deity
on Christmas Day a true and holy deity

Went down to Earth with human flesh
Went down to Earth with human flesh
for sacrifice

Rudolph the red nosed
Rudolph the red nosed reindeer
with it’s red belly
The all gracious king
the all gracious king
of all the Earth

For sinful men such a deity doth appear
And wink and nod in reply
as he winked and nod in reply

Rudolph the red nosed
Rudolph the red nosed reindeer
with it’s red belly
The all gracious king
the all gracious king
of all the Earth

Rudolph the red nosed
Rudolph the red nosed reindeer
with it’s red belly
The all gracious king
the all gracious king
of all the Earth

The wretched world is run by ox and ass
The wretched world is run by ox and ass
And in vain build I

A graph connecting various ideas would be super useful to beginner researchers.

I’m an undergraduate student studying software engineering and mathematics. I am graduating in April and thought it’s best I take advantage of my last semester to figure out if I want to really go to grad school.

So I asked one of my professors if he could do an independent study with me in machine learning and he said I could either do research or a project. My semester is only 4 months long. Do you have any suggestion of what I can pick as a topic?

I took an intro to AI course with this professor where we mostly learned some of the history of AI and lisp. I didn’t feel like I learned much. I have a very naive idea about ML and I’m really interested in learning more about it. I’m really interesting in learning some of the theories and math behind it. Do any of you have any good book suggestions? Maybe my independent study could be based on a book? He’s a comp sci professor but he has has a math degree.

I’m also applying to applied math programs for grad school to learn the theory and applications of machine learning.

Hello, new to ML and also not a very math-oriented person. I am creating a Discord bot that will be able to detect phishing links by using a decision tree (still need to figure out how to link the trained ML model to the bot).

The current accuracy of this program is 90% which seems pretty good on the surface but how can I tell if its *actually* 90%? I was reading about confusion matrixes and training via entropy, maybe either of those is good to use? Every run-through of the program the accuracy decreases. Why?

On the top line of my code you can see where I got my dataset which contains approximately 2000 instances. Is that enough? I found this dataset which contains 5000 instances https://data.mendeley.com/datasets/h3cgnj8hft/1 . Can I train the decision tree on more than one dataset? Is that a good idea? Should I combine both datasets into one?

Ultimately, what should be my next step(s)?

https://pastebin.com/XE0Ss9hq