I'm running a Julia 1.4 notebook, via Jupyter lab. I need to limit the notebook to a single logical CPU core, on average. In Python I use the following incantation to restrict Numpy's CPU usage: from threadpoolctl import threadpool_info,threadpool_limits with threadpool_limits(limits=1, user_api='blas'): >> code here << Would anyone happen to know whether its possible to similarly limit Julia's CPU usage from within a notebook?
I'm using Julia 0.6.3 with Dataframes.jl I was wondering if there was any way to get categorial features easily in Julia? For large datasets it can be impossible to enter everything by hand. My workaround is to rely on strings and usually low cardinality but it's not fool-proof. My workaround so far : cat_cols = [] for col in cols if contains(string(typeof(X_train[col])),"String") == true push!(cat_cols,col) end end But it seems kind of ugly and I don't catch label encoded values …
I currently have $1700+$ CSV files. Each of them is in the same format and structure, give or take a row or possibly a column at the end. Each CSV is $\approx 3.8$ MB. I need to perform a transformation on each file Extract one data set, perform a summation and column select, then store inside a folder. Extract an array, no need for column names here, then sore inside a folder. From an algorithmic POV, is it better to …
I am trying out POS tagging using RNN but not able to figure out what's wrong in my implementation because of which the gradient check is failing. Please help. I am pasting the relevant part below: # weights Wxh = randn(inputLayerSize, hiddenLayerSize)*0.01; # input to hidden Whh = randn(hiddenLayerSize, hiddenLayerSize)*0.01; # hidden to hidden Bh = zeros(1, hiddenLayerSize); # hidden bias Why = randn(hiddenLayerSize, outputLayerSize)*0.01; # hidden to output By = zeros(1, outputLayerSize); # output bias function forwardRNN(x, y, h, …
I have been using this library for basic neural network construction and analysis. However, it does not have support for building multi-layered neural networks, etc. So, I would like to know of any nice libraries for doing advanced neural networks and Deep Learning in Julia.
I'm working with what I guess is an older textbook that is using an older version of Julia as they are using the eye() function to create an identity matrix, which appears to not exist in the version I am currently using. What alternative is there for eye()? Is there a
I have a function to optimize with ca. 200 parameters + one constraint (sum of squares of the parameters must be equal one) This problem can be solved using Lagrange Multipliers and my intuition tells me, that methods that do that must be readily available. If I had a choice, I would prefer an algorithm existing on JuMP.jl
First, excuse me for the noob and long question which is probably doesn’t even belong to here, I know there are several question been answered like this out there, but I think this is going to be up-to-date. Stack Overflow deleted my question and redirected me to here. I study economics and finance on undergraduate level, and to be honest, I am not really into programming so far. However, I must admit it you can't doing really well nowadays without …
I've heard a lot of buzz for Julia programming language and how it's the future of AI. Should I buy into the hype and start learning Julia? Or do you think its better to stick to a better established language (e.g. python). What are the pros and cons of Julia compared to other languages aimed towards data science (python, R, etc.)?
I'm relatively new to data science, having done some machine learning projects at work. My background is in R, although I have some experience in Python. I'm considering choosing one language and getting really good at using it for data science / machine learning. Now, I've read various articles touting Julia's strengths - namely ease of use and massive speed benefits. It sounds almost too good to be true! So my question is, is there any particular reason(s) why I …
I am working on solving the handwritten digit recognition problem by implementing a neural network. But the accuracy of the network is coming out to be very low, around 11% for the train dataset. I am not sure what is wrong with my program. I tried changing the learning rate and the number of hidden units, but no luck. Could anyone please take a look and help me out with what I am missing? I am pasting my Julia code …
Is anyone using Julia (http://julialang.org/) for professional jobs? Or using it instead of R, Matlab, or Mathematica? Is it a good language? If you have to predict next 5-10 years: Do you think it grow up enough to became such a standard in data science like R or similar?