ATNW-4118
Artificial Neural Networks
BSCS-8 / BSSE-8
Lecture # 2

Python Introduction
• Interpreted high-level programming language for general-purpose programming
• Developed by Guido van Rossum
• First released in 1991
• Free ware
• Determines variable types implicitly
• Relies on indentation as a control structure
• Developer is not forced to define classes
• By default installed on Mac OS X and Linux
• Using Windows, download the latest version of Python from:
• http://www.python.org
• For Jupyter notebook:
• https://jupyter.org/install
Instructor: Tanzila Kehkashan
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Python IDEs
• Spyder
• Open source cross-platform IDE for data science
• Integrates the essentials libraries for data science, such as NumPy, SciPy,
Matplotlib and IPython
• Jupyter Notebook
• It is a web application based on the server-client structure, and it allows you to
create and manipulate notebook documents - or just “notebooks”.
• RStudio
• IDE for R, a programming language for statistical computing and graphics
• Orange
• Open-source data visualization, machine learning and data mining toolkit
• VSCode
• Includes support for debugging, embedded Git control and GitHub, syntax
highlighting, intelligent code completion, snippets, and code refactoring
Instructor: Tanzila Kehkashan
3

Python Libraries
1. TensorFlow
• developed by Google
• Is used in almost every Google application for machine learning
• For writing new algorithms that involve a large number of tensor operations
• Easily Trainable on CPU as well as GPU for distributed computing
• You can train multiple neural networksand multiple GPUs which makes the models
very efficient on large-scale systems
2. Scikit-Learn
• Associated with NumPy and SciPy
• Contains a numerous number of algorithms for implementing standard machine
learning and data mining tasks like reducing dimensionality, classification,
regression, clustering, and model selection.
• Cross-validation: There are various methods to check the accuracy of supervised
models on unseen data
• Offers many unsupervised learning algorithms e.g; clustering, factor analysis,
principal component analysis to unsupervised neural networks.
Instructor: Tanzila Kehkashan
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Python Libraries
• Useful for Feature Extraction from images and text (e.g. Bag of words)
3. Numpy
• TensorFlow and other libraries uses Numpy internally for performing multiple
operations on Tensors.
• Array interface is the best and the most important feature of Numpy.
• Can be utilized for expressing images, sound waves, and other binary raw streams
as an array of real numbers in N-dimensional.
4. Keras
• Provides some of the best utilities for compiling models, processing data-sets,
visualization of graphs, and much more
• Keras is comparatively slow
• runs smoothly on both CPU and GPU
• In use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others.
• Popular among startups that place deep learning at the core of their products.
• provides many pre-processed data-sets and pre-trained models like MNIST, VGG,
Inception, SqueezeNet, ResNet etc
Instructor: Tanzila Kehkashan
5

Python Libraries
5. PyTorch
• primarily used for applications such as NLP
• It is primarily developed by Facebook’s artificial-intelligence research group and
Uber’s “Pyro” software for probabilistic programming is built on it.
6. LightGBM, XGBoost, and CatBoost
• helps developers in building new algorithms by using redefined elementary
models and namely decision trees.
7. Eli5
• Mathematical applications which requires a lot of computation in a short time
8. SciPy
• SciPy library contains modules for optimization, linear algebra, integration, and
statistics
• Solving mathematical functions
• Tasks including linear algebra, integration (calculus), ordinary differential equation
solving and signal processing
Instructor: Tanzila Kehkashan
6

Python Libraries
9. Theano
• For computing multidimensional arrays
• Can also be used on a distributed or parallel environments just similar to
TensorFlow
10.Pandas
• Have so many inbuilt methods for grouping, combining data, and filtering, as
well as time-series functionality
• Re-indexing, Iteration, Sorting, Aggregations, Concatenations and
Visualizations
11.NLTK
• Natural language toolkit and commonly called the mother of all NLP libraries.
• It is one of the mature primary resources when it comes to Python and NLP.
Instructor: Tanzila Kehkashan
7

Python Commands
Arithmetic Operators
• +, - , /, *, **
Comments
• # this is single line comments
• “““ this is
multiline comments”””
Instructor: Tanzila Kehkashan
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range() Function
• Generates arithmetic progressions
Control Flow – for statement
Instructor: Tanzila Kehkashan
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Control Flow – for statement
Sr.No. Control
Statement
Description
1 break statement Terminates loop statement and transfers execution to statement immediately
following loop.
2 continue
statement
Causes loop to skip remainder of its body and immediately retest its
condition prior to reiterating.
3 pass statement Pass statement in Python is used when a statement is required syntactically
but you do not want any command or code to execute.
Instructor: Tanzila Kehkashan
10

Defining Functions
>>> def f(x):
... return x*x
...
Function header
Function body
>>> f(2)
4
>>> f(2.5)
6.25
Function call
def function-name(Parameter list):
statements, i.e. the function body
• Syntax
• Example
• Arbitrary Number of Parameters
def arbitrary(x, y, *more):
print "x=", x, ", y=", y
print "arbitrary: ", more
>>> arbitrary(3,4)
x= 3 , y= 4
arbitrary: ()
>>> arbitrary(3,4, "Hello World", 3 ,4)
x= 3 , y= 4
arbitrary: ('Hello World', 3, 4)
Instructor: Tanzila Kehkashan
11

Defining Functions
Instructor: Tanzila Kehkashan
12

Modules (files)
• Use any text editor to type the code into a file with extension .py
• To test the code, import it into a Python session and try to run it
• indentation is still important
• Syntax
• File’s extension (.py) is omitted in the import command
from file-name import function-name
Instructor: Tanzila Kehkashan
13

Python Built-in Data Structures
• Lists are enclosed in brackets. Tuples are enclosed in parentheses. Dictionaries are
built with curly brackets.
l = [1, 2, "a"]
t = (1, 2, "a")
d = {"a":1, "b":2}
Data Structure Sequence Data Can Contain
1 Lists Ordered Mutable Any type of objects
2 Tuples Ordered Immutable Any type of objects
3 Strings Ordered Mutable Only characters
4 Dictionaries Unordered Keys immutable Any type of objects
5 Sets Unordered Mutable Any type of unique
objects
6 Frozensets Unordered Immutable Any type of unique
objects
Instructor: Tanzila Kehkashan
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Instructor: Tanzila Kehkashan
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