simon pure-rgb

Step by step tuts to setup apache spark ( pyspark ) on linux and setup environment for deep learning with Apache Spark using Deep-Learning-Pipelines.

Step 1 : Install Python 3 and Jupyter Notebook

Run following command. Someone may need to install pip first or any missing packages may need to download.

sudo apt install python3-pip sudo pip3 install jupyter

Let's consider a folowing 4D matrix.

import numpy as np
W = np.random.randn(2,2,3,100)

print(W.shape)

output:

Briefly:

np.dot is the dot product of two matrices.

|A B| . |E F| = |A*E+B*G A*F+B*H|
|C D|   |G H|   |C*E+D*G C*F+D*H|

Whereas np.multiply does an element-wise multiplication of two matrices.

map and filter function in python is pretty different because they perform very differently. Let's have a quick example to differentiate them.

map function

Let's define a function which will take a string argument and check whether it presents in vowel letter sequences.

def lit(word):
    return word in 'aeiou'

Now let's create a map function for this and pass some random string.

For left alignment

 <img align="left" width="600" height="200" src="https://www.python.org/python-.png">

For right alignment

<img align="right" width="600" height="200" src="https://www.python.org/python-.png">

And for center alignment

We can delete a key from a Python dictionary by the some following approaches.

Using the del keyword; it's almost the same approach like you did though -

     myDict = {'one': 100, 'two': 200, 'three': 300 }
     print(myDict)  # {'one': 100, 'two': 200, 'three': 300}
     if myDict.get('one') : del myDict['one']
     print(myDict)  # {'two': 200, 'three': 300}

NumPy 1.7 (when np.pad was added) is pretty old now (it was released in 2013) so even though the question asked for a way without that function I thought it could be useful to know how that could be achieved using np.pad.

It's actually pretty simple:

>>> import numpy as np
>>> a = np.array([[ 1.,  1.,  1.,  1.,  1.],
...               [ 1.,  1.,  1.,  1.,  1.],
...               [ 1.,  1.,  1.,  1.,  1.]])
>>> np.pad(a, [(0, 1), (0, 1)], mode='constant')

array([[ 1., 1., 1., 1., 1., 0.],

Task

Set-up Raspberry Pi headlessly in Laptop, no HDMI no Monitor.

Necessary Software

RASPBIAN STRETCH ISO file
Etcher
Advance IP scanner
MobaXterm - By Choosing it
- No Need PuTTY - exceptionally because already built-in.
No Need VNC - already built-in.

Some GitHub Extensions that I use. They may also enable you to improve your productivity on GitHub.

Octotree allows easy exploration of the source code within the browser through a panel on the left.

2 github-dashboard provides the ability to filter events on the Github.com activity dashboard.

	import numpy as np
	import tensorflow as tf
	from tensorflow import keras
	from tensorflow.keras import Model, Input
	from tensorflow.keras.layers import Conv2DTranspose
	from tensorflow.keras.layers import UpSampling2D
	from tensorflow.keras.layers import Conv2D
	from tensorflow.keras.layers import BatchNormalization
	from tensorflow.keras.layers import Activation
	from tensorflow.keras.layers import Concatenate