Interested in this College?
Get updates on Eligibility, Admission, Placements Fees Structure

Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

Deep learning is a powerful technology that is being used to solve problems in many different areas. Deep Learning is a 10-week-long online certification course which is brought to you by Carnegie Mellon University’s School of Computer Science Executive and Professional Education along with the online education provider Emeritus. This online course will teach you the basics of deep learning and how to apply it to real-world problems. With the help of this course, you will be able to understand how neural networks operate, identify the right architecture for your needs, and generate and refine deep learning models.

The Deep Learning certification by Carnegie Mellon University’s School of Computer Science will teach you the basics of deep learning and how to apply it to real-world problems. You will learn about different neural network architectures, including CNNs and RNNs. You will also learn about the concepts necessary to solve time-based problems.

Also Read: Online Deep Learning with Python Certification Courses

The Highlights

10 weeks online course
Offered by Carnegie Mellon University
Programming Assignments
Capstone project
Peer Discussion

Programme Offerings

Certificate of completion
Online Classes
Lecture videos
Knowledge checks
Flexibility

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	CMU Pittsburgh

The Deep Learning certification fee is US$2,500. There are flexible payment options available, so you can pay the course fee all at once or in instalments.

Deep Learning Certification Fee Structure

Particulars	Total Fee
Pay in full	US$2,500
Pay in 2 instalments	US$2,613
Pay in 3 instalments	US$2,688

Eligibility Criteria

Certification Qualifying Details

Upon successful completion of this course, you will receive the Deep Learning certification by Carnegie Mellon University’s School of Computer Science.

What you will learn

After completing the Deep Learning certification syllabus, you will be able to understand how neural networks operate and identify the right architecture for your needs. Also, you will be able to understand the concept of neural network architectures.

Upon completion of the Deep Learning training, you will learn how to generate and refine deep learning models and solve prediction and identification problems. You will also learn the necessary concepts for solving time-based problems.

Who it is for

AI DeveloperSoftware DeveloperData AnalystData ScientistSoftware EngineerMachine Learning EngineerData Engineer

The Deep Learning online course is designed for participants who want to gain a deeper understanding of neural networks and develop the skills to solve complex problems using deep learning. This course is mainly useful for

Admission Details

Candidates can follow these instructions to join the Deep Learning classes:

Step 1: Go through the URL below: https://execonline.cs.cmu.edu/deep-learning

Step 2: Fill out the required details

Step 3: Download the brochure

Step 4: Go through the course page and click on ‘Apply Now’

Step 5: Fill out the application form online and pay the course fee

Application Details

To enrol in the Deep Learning certification course, you must complete the online application form on the official provider website.

The Syllabus

Describe significant impacts and successful use cases of neural networks in contemporary society
Describe the origins of modern connectionist neural networks with respect to early models of human cognition
Describe the structure and function of a perceptron

Describe a multilayer perceptron as a function with tunable parameters
Characterize learning as the process of estimating model parameters to minimize error between the network function and the target function
Define a gradient as a vector of partial derivatives

Describe the relationship of the learning rate and the ability of gradient descent to converge to a global minimum of a function
Describe incremental updates (i.e., stochastic gradient descent) to learn network parameters
Explain the necessary conditions for stochastic gradient descent to converge

Describe the need for shift-invariant pattern detection
Describe the process of shift-invariant pattern detection using a scanning neural network
Explain the training of neural networks with shared parameters

Summarize the backpropagation process through flat, convolutional, and pooling layers of a convolutional neural network
Explain how to compute derivatives for the affine map in convolutional layers of a convolutional neural network through backpropagation
Explain the dependency paths between individual elements of an activation map and the loss

Describe the types of problems that require recurrence in neural networks
Explain the pictorial representation of recurrent neural networks used in this program
Explain why recurrent connections are needed to refer to historical trends and patterns

Describe the architecture and training process for a time-synchronous recurrent neural network
Describe the greedy approach to decoding the output of an order-synchronous but time-asynchronous recurrent network
Identify the role of alignment in terms of computing divergence between input and output for an order-synchronous but time-asynchronous recurrent network

Explain how to compute embeddings of words from one-hot encodings, using language prediction with neural networks
Describe the architecture of a recurrent neural network used for language prediction
Describe the synchrony problem of sequence-to-sequence models

Describe the problem of overlapping classes
Explain the relationship of the logistic regression model and a perceptron with a sigmoid activation function
Describe how the maximum likelihood estimate is used to learn the parameters of a logistic regression model

Describe the architecture and purpose of a multi-head self attention block in the context of encoders
Summarize steps used to train the encoder and decoder of a variational autoencoder
Contrast properties of variational autoencoders with generative adversarial networks

CMU Pittsburgh Frequently Asked Questions (FAQ's)

1: What are the prerequisites for the Deep Learning certification course?

This course requires basic programming knowledge (Python or R), math skills (linear algebra and calculus), and an interest in machine learning and artificial intelligence.

2: What is the format of the Deep Learning classes?

The course is delivered online and consists of video lectures, hands-on exercises, and programming assignments.

3: How long does it take to complete the Deep Learning online course?

The course can be completed in 10 weeks which requires 10-15 hours of learning per week.

4: What is the process for earning the Deep Learning certification?

To earn the certification, you must complete all ten modules in this online course.

5: Who can benefit from Deep Learning?

Professionals like software developers, data scientists, and AI developers can benefit from this course.