Manash Kumar Mandal manashmandal

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Probably Coding || ! Probably Coding

manashmandal / nlp.py

Created November 10, 2017 20:12

	# LSTM for sequence classification in the IMDB dataset
	import numpy
	from keras.datasets import imdb
	from keras.models import Sequential
	from keras.layers import Dense
	from keras.layers import LSTM
	from keras.layers.embeddings import Embedding
	from keras.preprocessing import sequence
	# fix random seed for reproducibility
	numpy.random.seed(7)

manashmandal / keras.py

Last active November 9, 2017 13:14

	from keras.models import Sequential
	from keras.layers import Dense, Activation
	from sklearn.datasets import load_digits

	# Loading digits
	x, y = load_digits(n_class=10, return_X_y=True)

	# Convert to categorical
	yhot = keras.utils.to_categorical(y, num_classes=10)

manashmandal / radio.py

Last active November 3, 2017 17:33

radio player

manashmandal / build_opencv_ARM_cross

Created October 15, 2017 10:51 — forked from hrshovon/build_opencv_ARM_cross

Cross compile opencv3.3.0 for your raspberry pi and similar ARM devices with python support

	This is a note on how to cross compile opencv for pretty much any ARM device(HardFP supported in this case) and deploy. Native
	compiling in ARM devices can be painfully slow and they seem to hang often during build(mine got stuck at 43%). So if you happen
	to have a desktop/laptop/server running ubuntu or similar linux distro, u can build opencv in fractionth of the time taken for
	native compiling without any issues.
	Building opencv3 with TBB and NEON and VFP support can boost opencv performance. Thanks to Adrian at pyimagesearch for pointing that out.
	Both my PC and target machine aka orange pi zero are running ubuntu 16.04 with python2.7 and python 3.5.

	1.Run the following commands in both machines to install the necessary libraries etc.(mine worked with them,so they should be enough
	hopefully)
	sudo apt-get update && sudo apt-get upgrade

manashmandal / softmax_grad.py

Created September 19, 2017 11:02

Computing Gradient By Equation

	import tensorflow as tf

	# Import MNIST data
	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

	# Parameters
	learning_rate = 0.01
	training_epochs = 10
	batch_size = 100

manashmandal / softmax_tf_gradients.py

Last active September 18, 2017 14:31

Softmax using tf.gradients

	import tensorflow as tf

	# Import MNIST data
	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

	# Parameters
	learning_rate = 0.01
	training_epochs = 10
	batch_size = 100

manashmandal / TensorFlowSoftmax.py

Created September 18, 2017 14:17

Softmax Regression

	import tensorflow as tf

	# Import MNIST data
	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

	# Parameters
	learning_rate = 0.01
	training_epochs = 10
	batch_size = 100

manashmandal / l2norm_softmax.py

Created September 13, 2017 14:58

	# Actual Code : https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb
	# Modified By: Manash

	from __future__ import division, print_function, absolute_import

	# Import MNIST data
	from tensorflow.examples.tutorials.mnist import input_data
	mnist = input_data.read_data_sets("/tmp/data/", one_hot=False)

	import tensorflow as tf

manashmandal / index.html

Created August 11, 2017 16:51

d3plus 2.0

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>Document</title>

	<style>
	#exampleIframe {

manashmandal / index.html

Created August 11, 2017 16:13

viz

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>D3plus 1.0 Example</title>

	<!-- Adding D3 and D3plus Libraries -->
	<script src="https://d3plus.org/js/d3.js"></script>