Offered By in collaboration with

offer
INSTITUTIONAL BUNDLE OFFER PER STUDENT
elective1
Course 1 ₹ 17,000/-
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elective
Course 2 ₹ 17,000/-
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elective
Course 3 ₹ 17,000/-
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elective
Course 4 ₹ 17,000/-
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Choose any 4 courses
Total Price
₹ 68,000/-
Offer Price
₹ 42,000/-
Offer available only for Institutions

Course Details

Course Start Date
January 2020
Course End Date
May 2020
Language
English
Exam Dates
Multiple slots in May 2020
Credits & LTP
Each course offered would have 5 credit points.
Lecture
3
Tutorial
1
Practical (Hands-on)
1
Credit
5
Recommended For:
Graduation Programme - B.E./B.Tech
Branch - Computer Science Engineering and Information Technology, Electronics and Communication Engineering, Mechanical Engineering
Semester -
5 th,  6 th,  7 thand  8 th

Course Summary

Application of Deep Learning and Neural Networks is a course that teaches the basic and advanced concepts of deep learning and neural networks supported by industry relevant business case studies. The course is designed as per the latest industry trends and uses Python as the programming environment. Datasets required for the case studies can be obtained from the internet.

The course leverages the following to assist learners in building their practical and applied skills:

  • Object identification and recognition using Convolutional Neural Networks (CNN)
  • Face recognition using CNN (ResNet, VGG19)
  • Sentence similarity using embeddings from Word2Vec, LASER
  • Sentiment Analysis of tweets from social media using Long Short-Term Memory Networks (LSTM) & CNN
  • Similar question detection using Siamese networks

Hands-On

Course includes access to virtual hands-on environment.

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RECOMMENDED PRIOR KNOWLEDGE
1. Mathematics
  • Linear Algebra: Matrices and Vectors
  • Calculus: Differentiation, Partial Derivatives and Gradient
  • Statistics: Normal Distribution, Probability
2. Python
  • Basic Programming
  • Data Processing using NumPy, Scipi, Matplotlib and Pandas
  • Basic usage of Scikit, Scikit-learn packages in Python
3. Basic understanding of Machine Learning is preferred

Course Syllabus

The course syllabus will be delivered through a combination of multimodal digital content, live lectures, community based digital classrooms as applicable.
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Course Components

Digital Content in
Multimodal Format
Digital course accessible anytime and anywhere View Sample
Digital
Lectures
Live lectures delivered by industry and academic experts View Sample
Academic Connect
Community
Focussed on building conceptual clarity View Sample
Industry Connect
Community
Focussed on building industry oriented applied knowledge View Sample
Industry
Assignment
Access to two industry related mini projects View Sample
Practice
Assessment
Access to two practice assessments for self-evaluation View Sample
Summative
Assessment
Test of theoretical and applied knowledge View Sample
Verifiable Digital
Certificate
Verifiable certificate on successful completion View Sample
Internship
Opportunity
For top percentile, subject to vacancy View Sample
Job
Opportunity
Visibility to job vacancies with leading corporate recruiters View Sample

Available Locations

!~StateCity~!
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Meet the Mentors

Have a Question?

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FAQs

1. How will this programme help students in building their proficiency? +
  • This programme is envisaged to help build a strong foundation across various streams through digital lectures, practice and summative assessments, mini-projects, mentorship from renowned industry and academic experts. Students will also get an opportunity to showcase their skills to potential recruiters.