Aditya Jain adityajn105

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Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

1. lambda Function

It is an anonymous function which can be used as a parameter function with sorted/sort,bisect,map,filter,reduce

>> lambda x: x*2;
>> lambda (x,y) : x+y

2. map

It is used to perform some operations on all items of sequence in one line and get a map object is iterable and can be converted to sequence

	import numpy as np

	from keras.datasets import mnist
	from keras.models import Sequential
	from keras.layers import Dense,Conv2D,MaxPooling2D
	from keras.layers import Flatten,Dropout,Activation

	#create a model
	model = Sequential()
	model.add(Conv2D(64,(5,5),input_shape=(28,28,1),data_format="channels_last",strides=(1,1),padding="valid",activation="relu"))

	# Mathieu Blondel, September 2010
	# License: BSD 3 clause

	import numpy as np
	from numpy import linalg
	import cvxopt
	import cvxopt.solvers

	def linear_kernel(x1, x2):
	return np.dot(x1, x2)

	import numpy as np

	from keras.models import Sequential
	from keras.layers.core import Dense,Activation,Dropout
	from keras.datasets import mnist
	from keras.utils import np_utils

	no_epochs = 30
	batch_size=150 #1000
	v_length = 784

	"""
	Author : Aditya Jain
	Contact: [email protected]
	"""

	import matplotlib.pyplot as plt
	from matplotlib import style
	style.use('ggplot')

	import numpy as np

	import tensorflow as tf

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

	n_nodes_hl1 = 500
	n_nodes_hl2 = 500
	n_nodes_hl3 = 500
	n_classes = 10
	batch_size = 100

	### Download a file to Local System from Colab
	from google.colab import files
	with open('example.txt', 'w') as f:
	f.write('some content')
	files.download('example.txt')

	#======================================================================================================
	#upload file to Colab from Local System
	from google.colab import files
	uploaded = files.upload()

	SQL COMMANDS TYPE
	1. DDL (Data Definition Language)
	Data Definition Language, DDL, is the part of SQL that allows a database user to create and restructure database objects,
	such as the creation or the deletion of a table.
	CREATE TABLE, ALTER TABLE, DROP TABLE, CREATE INDEX, ALTER INDEX, DROP INDEX, CREATE VIEW, DROP VIEW
	2. DML (Data Manipulation Language)
	Data Manipulation Language, DML, is the part of SQL used to manipulate data within objects of a relational database.
	INSERT, UPDATE, DELETE
	3. DQL (Data Query Language)
	SELECT