Note: I'm currently taking a break from this course to focus on my studies so I can finally graduate
Install deps
$ sudo pip install cogapp
$ sudo pip install jinja2
Run COG
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""" | |
client.py - AsyncIO Server using StreamReader and StreamWriter | |
This will create 200 client connections to a server running server.py | |
It will handshake and run similar to this: | |
Server: HELLO | |
Client: WORLD |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
Taught by Brad Knox at the MIT Media Lab in 2014. Course website. Lecture and visiting speaker notes.
- Power to the People: The Role of Humans in Interactive Machine Learning by Knox, Cakmak, Kulesza, Amershi, and Lau
- A Few Useful Things to Know about Machine Learning by Domingos
- Machine Learning that Matters by Wagstaff
- Beyond Concise and Colorful: Learning Intelligible Rules by Pazzani et al.
- [Designing Games with a Purpose] (https://www.cs.cmu.edu/~biglou/GWAP_CACM.pdf) by von Ahn and Dabbish
- [Human Model Evaluation in Interactive Supervised
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from bitsandbytes.nn.modules import Linear8bitLt, Linear4bit | |
from contextlib import contextmanager | |
def noop (x=None, *args, **kwargs): | |
"Do nothing" | |
return x | |
@contextmanager | |
def no_kaiming(): | |
old_iku = init.kaiming_uniform_ |