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Intent - based Networking

Intent-based networking (IBN) is a form of network administration that incorporates artificial intelligence (AI), network orchestration and machine learning (ML) to automate administrative tasks across a network. The goal of IBN is to reduce the complexity of creating, managing and enforcing network policies and reduce the manual labor associated with traditional configuration management. For example, an IBN command may look like this:

Allow accounting applications to access server ABC, but do not allow manufacturing applications to access.

The IBN management application will then determine which devices and routes match the business intention and make the appropriate configuration changes automatically.

Intent-based networking and software-defined networking (SDN) are similar in many aspects. Both approaches rely on a centralized controller to manage distributed devices on the network instead of individually managing each device from its own management console. Both approaches have the ability to understand network configuration and interaction across multiple devices.

Where the two approaches differ, however, is in how they are addressed at the administrator level. SDN continues to have a device-centric view of the network and commands are primarily about how devices should operate, but intent-based networking commands are issued from a business perspective. This second-level abstraction is the primary difference when it comes to intent-based networking vs. SDN.

Source : https://whatis.techtarget.com/definition/intent-based-networking-IBN?track=NL-1823&ad=931357&src=931357&asrc=EM_NLN_121593174&utm_medium=EM&utm_source=NLN&utm_campaign=20191219_Word%20of%20the%20Day:%20intent-based%20networking%20(IBN)

Contextual AI: The Next Frontier of Artificial Intelligence

https://theblog.adobe.com/contextual-ai-the-next-frontier-of-artificial-intelligence/

4 Waves of AI

TOP 10 IT JOBS in 2019

1. Artificial Intelligence Engineer ( Artificial Intelligence , Machine Learning and Deep Learning) 

2. Data Scientist (and Data Analyst) 

3. Python Developer

4. Cyber Security Engineers

5. Full Stack Developer

6. DevOps Engineer

7. Cloud and IoT  Engineer

8. Java Developer

9. Java Script Developer

10. Scrum Master

VTU PYTHON SYLLABUS

Module – 1 : Why should you learn to write programs, Variables, expressions and statements, Conditional execution, Functions –8Hours
Module – 2 : Iteration, Strings, Files –8 Hours
Module – 3 : Lists, Dictionaries, Tuples, Regular Expressions–8 Hours
Module – 4 : Classes and objects, Classes and functions, Classes and methods–8 Hours
Module – 5 : Networked programs, Using Web Services, Using databases and SQL–8 Hours

Course objectives: This course will enable students to
Learn Syntax and Semantics and create Functions in Python.
Handle Strings and Files in Python.
Understand Lists, Dictionaries and Regular expressions in Python.
Implement Object Oriented Programming concepts in Python
Build Web Services and introduction to Network and Database Programming in Python.

Course outcomes: The students should be able to:
Examine Python syntax and semantics and be fluent in the use of Python flow control and functions.
Demonstrate proficiency in handling Strings and File Systems.
Create, run and manipulate Python Programs using core data structures like Lists, Dictionaries and use Regular Expressions.
Interpret the concepts of Object-Oriented Programming as used in Python.
Implement exemplary applications related to Network Programming, Web Services and Databases in Python.

Question paper pattern:
The question paper will have TEN questions. There will be TWO questions from each module. Each question will have questions covering all the topics under a module. The students will have to answer FIVE full questions, selecting ONE full question from each module.

Text Books:
1. Charles R. Severance, “Python for Everybody: Exploring Data Using Python 3”, 1st Edition, CreateSpace Independent Publishing Platform, 2016. (http://do1.drchuck.com/pythonlearn/EN_us/pythonlearn.pdf ) (Chapters 1 – 13, 15)

2. Allen B. Downey, "Think Python: How to Think Like a Computer Scientist”, 2ndEdition, Green Tea Press, 2015. http://greenteapress.com/thinkpython2/thinkpython2.pdf) (Chapters 15, 16, 17)(Download pdf files from the above links)

Reference Books:

1. Charles Dierbach, "Introduction to Computer Science Using Python", 1st Edition, Wiley India Pvt Ltd. ISBN-13: 978-8126556014
2. Mark Lutz, “Programming Python”, 4th Edition, O’Reilly Media, 2011.ISBN-13: 978-9350232873 3. Wesley J Chun, “Core Python Applications Programming”, 3rd Edition,Pearson Education India, 2015. ISBN-13: 978-9332555365
4. Roberto Tamassia, Michael H Goldwasser, Michael T Goodrich, “Data Structures and Algorithms in Python”,1stEdition, Wiley India Pvt Ltd, 2016. ISBN13: 978- 8126562176
5. Reema Thareja, “Python Programming using problem solving approach”, Oxford university press, 2017 

List of Machine Learning Algorithms

     1.    Find S Algorithm

     2.    List then Elimination Algorithm

     3.    Candidate Elimination Algorithm
4.    Rote Learner
5.    The Basic Decision Tree Learning Algorithm (ID3)
6.    Gradient Descent Algorithm
7.    The Backpropagation Algorithm
8.    Brute Force MAP Learning Algorithm
9.    Gibbs Algorithm
10. Naïve Bayes Classifier for learning and classifying text
11. Bayesian Network Algorithm
12. The EM Algorithm
13. K- Means Algorithm
14. K-NN Algorithm
15. Non Parametric Locally Weighted Linear Regression Algorithm
16. Q – Learning assuming deterministic rewards and actions.