i.e.
mapping system.
dog = picture A of dog
breaks down if someone does Dog walking in th epart next to a black cat
CNN in theory could map any # of mappings automatically to any number of images and would grow and change along with the content and inputs that people use.
For the vast majority of systems you still really just need a mapping system
CMS systems aren't going anywhere
They are good for when there is only 1 answer to a question
can anyone tell me to do some practicals related to data mining and ml quickley, which tutorial you preffer ? I just need to play on some Data Science related stuffs.
This will be my first hands on experince, I'd like to do this untill I familiar with the project what I'm gonna do in campus
@AMFIRNAS This course should be good for getting hands on experience with deep learning: http://www.fast.ai/
And this course from Andrew Ng can give you more basic knowledge about Machine Learning: https://www.coursera.org/learn/machine-learning
Hi All! Can someone please help me with an issue?
I'm training a recurrent neural network (with GRU) for a classification problem using rmsprop as an optimizer.
I'm training a recurrent neural network (with GRU) for a classification problem using rmsprop as an optimizer.
Training loss goes down for the first ~1 million examples, but then starts going up again
Why could it be?
How sure are you that your dataset is right?
And aslso the model might be too small
(I am a student so please take it as a grain of salt)
The dataset is probably noisy. If I reduce dataset size to few hundred thousand examples I get training accuracy above 90%. But even if the dataset is noisy, can it lead to training error increasing over time? I've thought if model capacity is large enough training error should decrease to near 0 (memorize the training set), if capacity is small it should stay flat at some point.
"Understanding Deep Learning Requires Rethinking Generalization" paper shows how neural networks can memorize even random labels
hey i just read quickly about machine learning course needed, there were discounts at udemy, i just registered myself : https://www.udemy.com/machinelearning/learn/v4/overview
its limited time
Feel free to read through my notes and if you have any questions, let me know.
The Goldman Sachs Senior Data scientists I talked to was a really cool guy
Hi, I want to classify a multi labeled data using deep learning techniques like CNN without building multiple classifier for each label.. when I read about it they say that I should use multiple sigmoid units on the last layer with binary cross entropy loss function.. actually I didn't understand why this would work and is there a better way to do this?
I'm availabe for at least the next hour to help out with whatever.
And if any of you know an easy way to parallelize my R scripts I'd love to hear it. ;)
first step is to change your label data into a binary set
once you get the data out of a one field with multiple values and into multiple fields with binary values it's much easier
one sec let me see if I can find an example.
Like this:
Is the R way
Also for n categorical to n binary columns... are we trying to make dummy variables here?
I ended up just using Fork instead of trying to do some complicated multithreading stuff.
;)