Deep Learning
offered by Carnegie Mellon University

Private University
140 acre campus
Estd. 1900

Deep Learning
at
Carnegie Mellon University
Overview

Identify and build the right neural network for solving your biggest challenges today—and in the future.

Duration	10 weeks
Total fee	₹1.46 Lakh
Mode of learning	Online
Difficulty level	Intermediate
Official Website	Go to Website
Course Level	UG Certificate

Deep Learning
at
Carnegie Mellon University
Highlights

Earn a Certification after completion
Knowledge Checks
Dedicated Program Support Team
Capstone Project
Peer Discussion

Deep Learning
at
Carnegie Mellon University
Course details

Skills you will learn

Data Science Transportation

Who should do this course?

This program is most suitable for:
Software Engineers
Software Developers
Data Scientists & Teams
AI & ML Professionals
Technology Professionals

What are the course deliverables?

Develop an understanding of deep learning techniques
Understand the structure, function, and training of key neural network architectures for building tools and systems
Build the confidence to apply deep learning methods to real-world problems

More about this course

Expertise in deep learning is an in-demand skill for technical positions in software engineering and data science.
Applications of artificial networks are wide-reaching and include solutions for problems in the language (speech recognition, translation), transportation (autonomous driving, real-time analysis), imaging (disease diagnosis, facial recognition), and many more areas across sports, and the healthcare industry.
Over the course of 10 weeks, you will gain an understanding of how neural networks operate and how to identify the right architecture for addressing your current and future challenges.

Deep Learning
at
Carnegie Mellon University
Curriculum

Module 1: Introduction and Universal Approximation

Module 2: Training Multilayer Perceptrons

Module 3: Stochastic Gradient Descent and Optimizers

Module 4: Basics of Convolutional Neural Networks (CNNs)

Module 5: CNNs: Training and Variants

Module 6: Basics of Recurrent Neural Networks (RNNs)

Module 7: Connectionist Temporal Classification and Sequence-to-Sequence Models

Module 8: Attention and Translation

Module 9: Representations and Autoencoders

Module 10: Transformers and Graph Networks, Variational Autoencoders, Generative Adversarial Networks

Deep Learning
at
Carnegie Mellon University
Faculty details

BHIKSHA RAJ

Bhiksha Raj works on a variety of areas related to AI, with a focus on speech processing, and more generally on intelligent systems that can learn to understand and respond to their acoustic environment.

Deep Learning
at
Carnegie Mellon University
Entry Requirements

Eligibility criteria Up Arrow Icon

The subject matter in this program is rigorous. To ensure success, participants must have a strong working knowledge of linear algebra, calculus, statistics, probability, and object-oriented programming including Python.

Conditional Offer Up Arrow Icon

Yes
The Universities' Heinz College occasionally admit students to the program with condition(s) placed on their enrollment. A condition will be clearly stated within the admission offer letter.