Introduction to Deep Learning

  • Categories Programing
  • Duration Instructor Led, Live - 3 Months Online
  • Fast Filling
  • Start Date September 2021

About Course

In this program we will learn about the basics of deep neural networks and their applications to various AI tasks. Advance your knowledge about deep neural networks through this online certificate program with cutting-edge content offered in association with Carnegie Mellon University and understand its application in industry practice.
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Learn how to apply neural networks and deep learning techniques to develop innovative solutions.

What we offer

Introduction to Deep Learning is a course that helps you gain footing in today’s complex job landscape.

Taught by Dr. Bhiksha Raj, this course provides you a blue-print to handle real-world challenges while imbibing autonomy to take decisions.

So what is the course about?

In this program we will learn about the basics of deep neural networks and their applications to various AI tasks. By the end of the program, it is expected that students will have significant familiarity with the subject and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.

What’s holding you back?

The program for Deep Learning will span 10 weeks with LIVE online, collaborative lectures covering different subareas in each week. They will be combined with assignments and project work to be completed by individuals and in groups.

Lecture Topics

Introduction to Neural Networks

  • A brief introduction with history
  • Neural networks as a universal approximator

Concepts behind training neural networks

  • The problem of training
  • Convergence issues and speed of training
  • Incremental updates and second order methods
  • Regularization and other tricks of the trade

Convolutional Neural Networks (CNNs)

  • Scanning with neural networks
  • Convolutional neural networks in detail
  • Training CNNs and transpose convolutions

Recurrent neural networks

  • Analyzing time series data with neural nets
  • Stability and convergence
  • Training recurrent nets
  • Connectionist temporal models
  • Attention models


Lab A

  • Building your own MLP from scratch
  • Building your own CNN from scratch
  • Building your own recurrent network

Lab B

  • Training a large MLP
  • Face identification and recognition
  • Speech recognition
  • Sequence-to-sequence conversion

Certificate of Completion

from Carnegie Mellon University

Join this online certificate program, offered by Carnegie Mellon University, to learn as well as upgrade your knowledge on artificial neural networks.

Course Curriculum

Module 1: Introduction to Neural Networks

  • History & cognitive basis of neural computation
  • Connectionist Machines
  • Rosenblatt’s Perceptron
  • Multilayer Perceptrons
  • The neural net as a universal approximator

Week: 1 & 2         Assessment: Quiz and Classroom Discussion


Module 2: Training Neural Networks

  • Training a Neural Network
  • Perceptron learning rule
  • Optimization by gradient descent
  • Convergence of perceptron algorithm
  • Back propagation, Calculus of back propagation
  • Convergence issues in back propagation
  • Second order methods
  • Convergence in Neural Networks and rates of Convergence
  • Loss surfaces
  • Learning rates, and optimization
  • Stochastic gradient descent
  • Overfitting and regularization
  • Tricks of The Trade:
    • Choosing a divergence (loss)
    • Batch normalization

Week: 3 & 4         Assessment: Curated Assignments, Quiz and TA Interactions


Learning to code networks:

  • Building your own CNN from scratch
  • Building your own MLP from scratch
  • Building your own recurrent network


Module 3: Convolutional Neural Networks: Scanning for Patterns

  • Convolutional Neural Networks (CNN)
  • Weights as templates
  • Translation invariance
  • Training with shared parameters
  • Arriving at the convolutional model
  • Models of vision
  • Neocognitron
  • Mathematical details of CNNs
  • Backpropagation in CNNs
  • Variations in the basic model
  • Some history of the ImageNet

Week: 5, 6 & 7         Assessment: Curated Assignments, Quiz and TA Interactions


Module 4: Recurrent Neural Networks: Scanning for Patterns

  • Recurrent Neural Networks (RNNs)
  • Modeling series
  • Back propagation through time
  • Bidirectional RNNs
  • Stability, Exploding/vanishing gradients
  • Long Short-Term Memory Units (LSTMs) and variants
  • Loss functions for recurrent
  • Sequence prediction, Sequence
    To Sequence Methods & Models
  • Labelling Unsegmented Sequence
  • Data with Recurrent Neural

Week: 8,9 & 10

Project Day / Buffer / Overflow

Assessment: Curated Assignments, Quiz and TA Interactions, Capstone Project


Using existing toolkits to solve real-world issues:

  • Training a large MLP
  • Face identification and recognition
  • Speech recognition
  • Sequence-to-sequence conversion

What Will I Learn?

  • Learn directly from Carnegie Mellon University’s top faculty
  • Develop intuitive skills through practice & collaboration
  • Gain confidence and accelerate your career
  • Build a career in a niche, growing domain

About the instructors

Dr. Bhiksha Raj, who earned his Ph.D. in electrical and computer engineering at Carnegie Mellon in 2000, has devoted his career to developing speech- and audio-processing technology. He has had several seminal contributions in the areas of robust speech recognition, audio analysis and signal enhancement, and has pioneered the area of priv...
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Material Includes

  • Live, online, certificate program for working professionals, taught by world-class faculty
  • Access to teaching assistants with prior industry experience
  • Certificate of completion offered by Carnegie Mellon University (Pittsburgh, USA)
  • Comprehensive program syllabus covers topics on the use of artificial intelligence
  • Hands-on learning & project work in a collaborative environment
  • Access to opportunities, network of academics, and other professionals in the field

₹ 1,95,000 + GST
No Cost EMI starting ₹19,175 pm


    • BTech/MTech degree in Computer Science, Engineering, or a related subject
    • Min. 3 years professional work experience
    • Knowledge of programming languages such as Java, C, C++, Python
    • Understanding of the internet, computer systems and networks
    • Ability to work independently and in teams

    Why Learn With Us

    • Practical, hands-on, LIVE online programs
    • Small class sizes with on-demand Teaching Assistants support
    • Collaborative knowledge sharing experiences
    • Capstone projects to acquaint you with industry relevant challenges
    • Access to Carnegie Mellon University's learning infrastructure for coursework
    • Collaboration with Million Minds to ensure a smooth talent transition