Skip to main content

Deep learning

This is where I write notes about code, applications, algorithms, everything related to deep learning


Word embeddings

Skip-gram models and negative sampling

Sigmoid, tanh, ReLU functions. What are they and when to use which?

Underfitting, Overfitting or Bias and Variance

Pytorch and Keras cheat sheets

Deep learning with Multi-GPUs





Comments

Post a Comment

Popular posts from this blog

Spam and Bayes' theorem

I divide my email into three categories: A1 = spam. A2 = low priority, A3 = high priority. I find that: P(A1) = .7 P(A2) = .2 P(A3) = .1 Let B be the event that an email contains the word "free". P(B|A1) = .9 P(B|A2) = .01 P(B|A3) = .01 I receive an email with the word "free". What is the probability that it is spam?
1 comment
Read more

Python Tkinter: Changing background images using key press

Let's write a simple Python application that changes its background image everytime you click on it. Here is a short code that helps you do that: import os, sys import Tkinter import Image, ImageTk def key(event): print "pressed", repr(event.char) event.widget.quit() root = Tkinter.Tk() root.bind_all(' ', key) root.geometry('+%d+%d' % (100,100)) dirlist = os.listdir('.') old_label_image = None for f in dirlist: try: image1 = Image.open(f) root.geometry('%dx%d' % (image1.size[0],image1.size[1])) tkpi = ImageTk.PhotoImage(image1) label_image = Tkinter.Label(root, image=tkpi) label_image.place(x=0,y=0,width=image1.size[0],height=image1.size[1]) root.title(f) if old_label_image is not None: old_label_image.destroy() old_label_image = label_image root.mainloop() # wait until user clicks the window except Exception, e: # Skip a
Post a Comment
Read more

Skip-gram model and negative sampling

In the previous post , we have seen the 3 word2vec models: skip-gram, CBOW and GloVe. Now let's have a look at negative sampling and what it is used to make training skip-gram faster. The idea is originated from this paper: " Distributed Representations of Words and Phrases and their Compositionality ” (Mikolov et al. 2013) In the previous example , we have seen that if we have a vocabulary of size 10K, and we want to train word vectors of size 300. Then the number of parameters we have to estimate in each layer is 10Kx300. This number is big and makes training prone to over-fitting and gives too much focus on words that appear often, and less focus on rare words. Subsampling of frequent words So the idea of subsampling is that: we try to maximize the probability that "real outside word" appears, and minimize the probability that "random words" appear around center word. Real outside words are words that characterize the meaning of the center word, wh
Post a Comment
Read more