Post Graduate Program In Deep Learning

Key Facts

Course Code: ND1803DL01 Duration: 10 hours/week x 17 weeks
Capacity: Cohorts of 25 per class Course Fee: 1,50,000/- INR + applicable GST
Medium of Instruction: English Location: AILABS, J-3, Block-GP, Sector -V, Salt Lake City, Kolkata - 700 091


Artificial Intelligence is transforming the world. Deep Learning, an integral part of this new Artificial Intelligence paradigm, is becoming one of the most sought after skills. If you wish to build a career in Artificial Intelligence, this Nano Degree will help you do so.


This course will provide a concrete awareness of the key concepts of Artificial Intelligence and showcase the techniques that form the current basis of Machine Learning and Deep Learning. It will cover the foundations and progressively build your knowledge base, which will converge to a series of lectures on Convolutional Neural Networks.

Payment Terms & Conditions

All fees are payable at the beginning of the course work. Payment mode - online transfer, debit card or credit card.

Eligibility Criteria

Bachelor's degree in Science, preferably in Physics, Mathematics, Statistics, Economics OR Bachelor's degree in Engineering.

Selection Procedure

Subject to satisfying the minimum academic qualification, selection of candidates will be based on performance in:
AILABS admission test. OR Valid CAT / GRE / GMAT score (within the last 3 years)
AND Interview by our academic panel.

All participants will be selected solely on the basis of merit.

Application Procedure

Course Structure

The course will be a blend of classroom learning augmented by additional content delivered through Audio Visuals, hands on coding, problem solving, invitational talks and workshops.


Module 1: Neural Networks and Deep Learning
Introduction to Deep Learning | Neural Networks Basics | Shallow Neural Networks | Deep Neural Networks

Module 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Practical aspects of Deep Learning | Optimization Algorithms | Hyperparameter tuning, Batch Normalization and Programming Frameworks

Module 3: Structuring Machine Learning Projects
Machine Learning Strategies - Part 1 | Machine Learning Strategies - Part 2

Module 4: Convolutional Neural Networks
Foundations of Convolutional Neural Networks | Deep Convolutional Models | Object Detection | Special applications

Admission Test Syllabus

Mathematical Reasoning (Standard XII)
Computational Reasoning

Assessment Procedure

The composite score in a course shall be the weighted average of the scores in lab-tests, assignments and practicum. The minimum composite score to pass a credit course is 45%. Certificate shall be awarded on successful completion of the course.

Binding Study Agreement

Each participant is required to attend at least 75% of all the classes, otherwise he/she will not be allowed to appear in the final examination.

If a participant fails to attend a course continuously for one week or more, he/she requires to furnish explanation with documentary evidence. The same applies for failure to take a class test.

Software & Tools

Libraries: TensorFlow, numPy, SciPy, Matplotlib, Scikit-Learn
Language: python
Distribution: Anaconda

Invitational Talks

Short invitational talks will be organized for each module,injecting the latest trends in research and industry.


Workshops will be organized, one in each month, where leading peers both from industries and academic institutions will be invited for orientation on AI application in different spheres.

Study Materials

Access to various online courses, list of reference books and journal papers shall be provided for reference at the beginning of each module. Participants should make effort to study online tutorials, as suggested by the faculty.

Information Camps

For any query on Nano Degree course, feel free to visit/call our counselors at:
Information Camp
J-3, Block-GP, Sector -V, Salt Lake City, Kolkata - 700 091

Contact Details

Post: AILABS, J-3, Block-GP, Sector-V, Salt Lake City, Kolkata - 700 091
Dial: +91 33 46034216
Email: |