Almost all the important Libraries and Frameworks explained in brief with their types with their applications and use cases.

1. Frameworks & Libraries
of
Machine Learning & Deep Learning

2. Table of Contents
1.ML Frameworks & Libraries
2.Jupyter Framework
3.Jupyter Application and Use Cases
4.Scikit Learn
5.Real World Applications of Scikit Learn
6.Modules of Scikit-Learn
7.Some of the algorithms used in Modules
8.Numpy
9.Pandas
10.Scipy Introduction
11.Scipy Task Specific Sub-Modules
12.Jupyter Libraries (Visualization)
13.Jupyter Libraries (NLP)
14.Zeppelin Framework
15.PySpark Framework
16.DL Frameworks & Libraries
17.TensorFlow
18.Keras
19.PyTorch
20.MXNet & Gluon
21.Chainer & CNTK
22.ONNX

3. Machine Learning Frameworks & Libraries
Frameworks
Jupyter
PySpark
Zeppelin
Natutal Language Processing
NLTK
Gensim
spaCy
General Libraries
Numpy
Scikit learn
Scipy
Pandas
Mllib
Visualization
Seaborn
Bokeh
Plotly
Matplotlib

4. Jupyter Framework
Jupyter User Interface
Jupyter Notebook
Jupyter Console
Jupyter QtConsole
Jupyter Console- It is a terminal
with kernels using Jupyter
protocol.
It can be installed with:
pip install jupyter -console
If you want to conda
Conda install -c conda-forge
jupyter-console
Jupyter Qt Console- The Qt console
is a lightweight application that largely
Looks like a terminal, but provides
enhancements possible in GUI
such as multiline editing, graphical
calltips and much more. It can
use any Jupyter kernal.
Jupyter Notebook – Jupyter Notebook
is a development environment for
writing and executing Python code.
The notebook is often used for analysis
with description and also for
executable documents to perform
data analysis.

5. Jupyter Applications and Use Cases
Application
Jupyter for Data Science
What causes cancer?
Is Ice-cream good for you?
Jupyter through large datasets
exploration prediction & inference
could give you the answer.
Jupyter kernals used in Data Science
Python, R, Julia, Bash
Use Cases
Notebook innovation of Netflix
PayPal Notebook
DevOps Intelligence with Jupyter Hub
Bloomberg B Quant Platform

6. Scikit Learn
Scikit Learn built for
machine learning library
is a Python module. Built
on Numpy, Scipy and
Matplotlib it contains
various algorithms in
Classification, Clustering,
Regression, Model Selection,
Dimensionality Reduction
and Preprocessing.
Dependencies
Python (>= 3.5)
Numpy (>= 1.11.0)
Scipy (>= 0.17.0)
Joblib (>= 0.11)
How to use
If Numpy & Scipy Installed
then use
pip install -U scikit-learn
Else
conda install scikit learn

7. Real world Applications of Scikit Learn
Visualizaing the stock market structure
Recognizing handwritten digits
Hierarchial clustering on image of coins
Face Completion with multi-output estimators
Comparing anamoly detection algorithms for outlier detection
Isotonic Regression
Compact Estimator Representation

8. Modules of Scikit Learn(Part1)
Biclustering
Clustering
Covariance Estimation
Cross Decomposition
Decomposition
Ensemble Methods
Feature Selection
Gaussian Process of Machine Learning
Missing Value Imputation
Generalized Linear Models
sklear.cluster.bicluster
sklearn.cluster
sklearn.covariance
sklearn.cross_decomposition
sklearn.decomposition
sklearn.ensemble
sklearn.feature_selection
sklearn.gaussian_process
sklearn.impute
sklearn.linear_model

9. Manifold Learning
Inspection
Gaussian Mixture Models
Model Selection
Multioutput Methods
Nearest Neighbors
Neural Networks
Pre-processing
Semi Supervised Classification
Super Vector Machines
Decision Trees
sklearn.manifold
sklearn.inspection
sklearn.mixture
sklearn.model_selection
sklearn.multioutput
sklearn.neighbors
sklearn.neural_network
sklearn.preprocessing
sklearn.semi_supervised
sklearn.svm
sklearn.tree

10. Some of the algorithms used in Modules
Spectral Co-Clustering algorithm- Rearranging the original
dataset after shuffling rows and columns to make bi-clusters contiguous.
Spectral Biclustering algorithm- Rows and columns of shuffled matrix
are rearranged to show the biclusters formed by the algorithm.
Feature Agglomeration- Similar features from different images
merged together to form a new image.
Affinity Propagation - Clustering by
passing messages between datapoints.
Sparse Inverse Covariance Estimation
Shrinkage Covariance Estimation

11. Numpy
It contains
1. useful linear algebra, Fourier transform,
and random number capabiliies
2. tools for integrating C/C++ and
Fortran code
3. sophisticated functions
4. a powerful N-dimensional array object.
To install NumPy package
pip install numpy
Use
Numpy is used as an efficient
multi-dimensional container
of generic data.
Numpy or “Numerical Python” is a Python
library that provides fundamental
package for Scientific computing.

12. Important Data Types
One Dimensional Array
Multi-Dimensional Array
Boolean
Integer
Float
Complex
Mathematical Functions
Add, Subtract, Multiple,
Divide, Power, Mod
Rounding,Ceil,Floor
Statistical
Trigonometric
Algebra

13. Pandas
Pandas is a open source Python library that provides data wrangling and analysis techniques to clean the data mess.
Implemented in 2008 by Wes Mckinney on top of C hence it’s fast.
To install Pandas use Python Package Installer Pip
pip install pandas
Pandas Functionality (General)
Reading CSV File
Reading Excel File
Head
Tail
Transpose
Describe
Pandas Functionality (Feature Engineering)
To check for Missing Values
To Drop Missing Values
To fill Missing Values – Direct Replace
To fill Missing Values – Backward Or Forward
Compare Elements in Percentage
Compute Standard Deviation
ComputeCovariance
Compute Correlation
Filtering Merging
Plotting Data Frames

14. Scipy is a high-level Scientific Computing that contains various toolboxes. It contains different submodules corresponding to
different applications such as interpolation, integration, optimization, image processing, statistics, special functions, etc.
Different Applications
File input/output
Special Functions
Linear Algebra Operations
Interpolation
Optimization and fit
Statistics and random numbers
Numerical Integration
Fast Fourier Transforms
Signal processing
Image Manipulation
scipy.io
Scipy.special
scipy.linalg
scipy.interpolate
scipy.optimize
scipy.stats
scipy.integrate
scipy.fftpack
scipy.signal
scipy.ndimage
Scipy

15. Scipy Task Specific Sub-Modules
scipy.cluster Vector quantization / Kmeans
scipy.constants Physical and mathematical constants
scipy.fftpack Fourier transform
scipy.integrate Integration routines
scipy.interpolate Interpolation
scipy.io Data input and output
scipy.linalg Linear algebra routines
scipy.ndimage N-dimensional image package
scipy.odr Orthogonal distance regression
scipy.optimize Optimization
scipy.signal Signal processing
scipy.sparse Sparse matrices
scipy.spatial Spatial data structures and algorithms
scipy.special Any special mathematical functions
scipy.stats Statistics

16. Jupyter Libraries (Visualization)
What Why How
Seaborn – Seaborn is a
Python library for
Statistical graphics.
Bokeh- Bokeh is a Python
library that target web
browsers for representation
and interactive visualization.
conda install seaborn
pip install seaborn
Conda install bokeh
pip install bokeh
Ease of representation online-
Easy to display charts online
Complex statistical plots are
easily plotted with simple commands
Transform visualizations from
seaborn, matplotlib and ggplot
Interactive – Graphs embedded
in html is fully ineteractive on
other web pages
Examines relationship between
multiple variables.
Visualize univariate or bivariate
distributions and for comparing
subsets of data.
An easy viwe of complex
datasets structure.
Build Comnples visualiations
of high level abstractions

17. pip install plotly
conda install -c plotly plotly=4.1.0
python -m pip install -U pip
python -m pip install -U matplotlib
Plotly-The plotly is an
interactive a Python library
that covers 40 unique chart
types ranging from statistical,
financial, geograohic,
scientifiic and 3-D usecases.
Matplotlib- Matplotlib is a
Python 2D plotting library
which produces quality figures
in variety of hardcopy formats
and interactive environments
across different platforms.
Basic Charts- Scatter Plots, Line Plot,
Bar Charts, PirCharts
Statistical and Seaborn-style Charts
Error Bars, Box Plots, Histograms,
Distplots
Scientific Charts- Contour Plots,
Heatmaps, Wind Rose Charts
Financial Charts -Time Series,
Candlestick Charts, Waterfall Charts
Lines, bars and markers such as
Stacked Bar Graph, Broken Barh,
plotting categorical Variables etc.
Plots in images contours and fields
Subplots, axes and figures
Statistics- Boxplot, Violin plots,
Pie and Polar Charts

18. Jupyter Libraries (NLP)
What Why
Convert text to lower case
word tokenize
sent tokenize
stop words removal
lemma
stemming
Get word frequency
NER(Named Entity Recognition)
How
Memory Independence- can process
large, web scale corpora
Efficient implemetations for Latent
sementic Analysis, Latent Dirichlet
Allocation or Random Projection
Similarity queries for documents in
their semeantic representation
NLTK- NLTK is a python library
for natural language processing
that works with human language
data along with text processing
libraries for classification, tokenization,
stemming, tagging, parsing.
Gensim- Gensim is a Python
library for topic modelling,
document indexing and
similarity retrieval. The
target audience are natural
language processing (NLP)
and information retrieval (IR).
pip install nltk
pip install gensim

19. pip install spacy
spaCy – A package for “Industrial
strength NLP in Python”.
It does not weigh the user
down with decisions over what
esoteric algorithms to use for common
tasks and it’s fast.
Has many convenience methods for
cleaning and normalising text.
Word-to-vector transformations
Dependency Parsing
Part-of-speech tagging

20. Zeppelin Framework
Apache Zeppelin is a
web-based notebook
that enables data-driven
interactive analytics and
collaborative documents
with Scala, SQL and much more.
Apache Zeppelin is build
on the JVM while Jupyter
is built on Python.
With Zeppelin there are possibilities
to mix languages across cells.
Zeppelin supports Scala, Python,
SparksSQL,Hive, Markdown and
Shell and we can make our own
language interpreter.
It is data exploration and
visualization intended for
big data and large scale projects.

21. PySpark Framework
Pyspark is great for
performing exploratory
data analysis at scale,
building machine learning
pipelines and to create
ETLs for data platform.
pip install pyspark
Libraries
Py4J – Popular library which is integrated
within Pyspark and allows to dynamically
interface JVM objects.
PySparkSQL- It is a PySpark library to apply
SQL-like analysis on huge structured and
semi-structured data.
Mllib - Mllib library uses the data parallelism
technique to store and work with data. It supports
many machine-learning algorithms for
Classification, regression, clustering,
collaborative filtering, dimensionality reduction,
and underlying optimization.

22. Deep Learning Frameworks & Libraries
Libraries
TenserFlow
PyTorch
Keras
MXNet
Caffe2
Gluon
Chainer
ONNX
CNTK
Frameworks
Jupyter
PySpark
Zappelin

23. TensorFlow
Tensorflow- TensorFlow is
computational framework
for building machine learning
models. We can do large and
complex computations easily
which is of high level and its
computation time is very optimized.
Implementations of
deep learning
Speech Recognition,
Computer Vision,
Robotics, Ir, NLP ETC.
Tensor – Tensor
is an N-dimensional
array of data.
Four main tensors
that we can create:
tf,Variable, tf.constant,
tf.placeholder and
tf.SparseTenser
TensorFlow Toolkit Hierarchy

24. Keras
Keras is a machine learning
framework specially useful
when you have a lot of data
and wants to go for AI:
deep learning.
It is useful for high-level API
Massive models of
deep learning are
easy to go in Keras
with single-line functions.
Keras does not block
access to lower level
frameworks.
Keras model
Serialization/Deserialization
APIs, callbacks, and
data streaming using
Python are very mature.
Keras is on higher level
not comparable to
Tenserflow which is
on lower level. On
Keras level the lower
level primitives are used
to implement Neural
network abstraction like
Layers and models.

25. PyTorch
The PyTorch is a
Software tool that
operates with
Dynamically updated
graph.
In PyTorch standard
debuggers can be
used such as pdb
or PyCharm.
The process of training
a neural network model
in PyTorch is simple
and clear and supports
data parallelism and
also supports
pre-trained models

26. MXNet & Gluon
MXNet – It is highly scalable deep
learning tool that can be used on
many devices.
It supports a large number of languages
( C++, Python, R, Julia, JavaScript, Scala,
Go and even Perl).
The framework is effectively parallel on
multiple GPUs and machines.
Fast Problem solving ability.
Gluon- It s a great deep learning framework
that can be used to create simple as well
as sophisticated models.
Specificity- Flexible interface and simplifies
prototyping, building and training deep learning
models without sacrificing speed to learn.
Based on MXNet and offers simple API
that simplifies creating deep learning model.
Brings together training algorithm and neural
network model thus providing flexibility in
development process without sacrifying performance.

27. Chainer & CNTK
Chainer- Chainer is first framework to use a dynamic
architecture model. It is written in Python on top of
the Numpy and CuPy libraries.
Chainer is faster than other Python oriented frameworks,
with TenserFlow the slowest inclusing MXNet and CNTK.
Better GPU data center performance than TenserFlow.
OOP like programming style.
CNTK is a Microsoft Cognitive Toolkit for
describing, training and executing computational
networks. It is an implementation of computational
networks that supports both CPU and GPU
CNTK inputs, outputs and parameters are
organised as tensers.
CNTK is also one of the first deep-learning
toolkits to support the Open Neural Network Exchange.
It can be included as library in Python,C#, or C++ programs.

28. ONNX
Built from the collaboration of Microsoft and Facebook as
a search for open format presentation of deep learning models.
It enables models to be trained in one framework inference
on the other when transferred.
Supported in Caffe2, Microsoft Cognitive Toolkit, and PyTorch.