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Nitish Bhardwaj nitish11

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Last active Aug 24, 2022
Shell scripting hacks!!!!
# Version : 1.0
# Author : Nitish Bhardwaj
# Description : This Script file contains some shell script quick hacks.
#Check below links to get basics
nitish11 / t-SNE Tutorial.ipynb
Created Dec 7, 2016 — forked from awjuliani/t-SNE Tutorial.ipynb
A notebook describing how to use t-SNE to visualize a neural network learn representations
View t-SNE Tutorial.ipynb
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nitish11 /
Last active Dec 4, 2018 — forked from baraldilorenzo/
VGG-16 pre-trained model for Keras

##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

# Ensure python 3 forward compatibility
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import theano
# By convention, the tensor submodule is loaded as T
import theano.tensor as T

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

So you're curious in learning this new thing called (Functional) Reactive Programming (FRP).

Learning it is hard, even harder by the lack of good material. When I started, I tried looking for tutorials. I found only a handful of practical guides, but they just scratched the surface and never tackled the challenge of building the whole architecture around it. Library documentations often don't help when you're trying to understand some function. I mean, honestly, look at this:

Rx.Observable.prototype.flatMapLatest(selector, [thisArg])

> Projects each element of an observable sequence into a new sequence of observable sequences by incorporating the element's index and then transforms an observable sequence of observable sequences into an observable sequence producing values only from the most recent observable sequence.