Deep Learning with Python and PyTorch

BY
IBM via Edx

Register yourself for Deep Learning with Python and PyTorch by edX and master various machine learning concepts and methods.

Lavel

Intermediate

Mode

Online

Duration

6 Weeks

Fees

Free

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
Learning efforts 2-4 Hours Per Week

Course overview

Deep Learning with Python and PyTorch by edX is part of a two-part series on developing Deep Learning models using Pytorch. The first course in the series is titled ‘PyTorch Basics for Machine Learning’, and it covers the basics of PyTorch. Hence, you must have completed the first course to understand this programme thoroughly.

Furthermore, the Deep Learning with Python and PyTorch course syllabus aims to teach how to build deep neural networks. You will learn methods such as initialisation, dropout, batch normalisation and different types of optimisers. The programme will further explore concepts like data whitening, deep autoencoders, shallow autoencoders, autoencoder applications, etc. After you are done with all the modules, you can test your skills through the final independent project.

The Deep Learning with Python and PyTorch certification falls under the subject of Data Analysis and Statistics. This is a six-week-long intermediate-level course that will not explain things from scratch. Thus, you must have prior knowledge and interest in the field of machine learning. There are two options to join the programme, namely, verified and free audit. In the verified mode, the learners will have unlimited access to the self-paced course and in the free mode, they will have access only for a limited period. 

The highlights

  • Provided by IBM
  • Verified certificate
  • Flexible deadlines
  • Data Analysis and Statistics
  • English transcripts
  • Self-paced course
  • Six-week duration
  • Free course to enrol
  • Intermediate course 
  • Graded quizzes

Program offerings

  • Intermediate course
  • Flexible deadlines
  • Offered by ibm
  • Duration of six weeks
  • Data analysis & statistics
  • 100% online
  • English transcripts
  • Professional certificate
  • Independent project
  • Verified certificate

Course and certificate fees

Type of course

Free

  • EdX’s Deep Learning with Python and PyTorch provides the option of auditing the course for free for a limited time period. Later, if you wish to gain the certificate, you can purchase it for a certain fee.  

Deep Learning with Python and PyTorch fee structure

Course Name

Fee in INR

Deep Learning with Python and PyTorch (Enrolment)

Free 

Deep Learning with Python and PyTorch (Certificate fee)

Rs. 8,267 /-

certificate availability

Yes

certificate providing authority

IBM

certificate fees

₹8,267

Eligibility criteria

Since the Deep Learning with Python and PyTorch programme is the second course in a two-part series, you must first complete the PyTorch Basics for Machine Learning course. Besides, you require knowledge of PyTorch Basics, Python and Jupyter notebooks, deep learning concepts, machine learning concepts.

What you will learn

Machine learning Knowledge of deep learning

The Deep Learning with Python and PyTorch certificate syllabus will help you learn about these concepts:

  • Deep neural networks
  • Methods of machine learning
  • Building deep learning pipelines
  • Computer vision networks

The syllabus

Module 1 - Classification

  • SoftMax Regression
  • SoftMax in PyTorch Regression
  • Training SoftMax in PyTorch Regression

Module 2 - Neural Networks

  • Introduction to Networks
  • Network Shape Depth vs Width
  • Back Propagation
  • Activation functions

Module 3 - Deep Networks

  • Dropout
  • Initialization
  • Batch normalization
  • Other optimization methods

Module 4 - Computer Vision Networks

  • Convolution
  • Max Polling
  • Convolutional Networks
  • Pre-trained Networks

Module 5 - Computer Vision Networks

  • Convolution
  • Max Pooling
  • Convolutional Networks
  • Training your model with a GPU
  • Pre-trained Networks

Module 6 - Dimensionality reduction and autoencoders

  • Principal component analysis
  • Linear autoencoders
  • Autoencoders
  • Transfer learning
  • Deep Autoencoders

Module 7 - Independent Project

Admission details

  • You can visit the Deep Learning with Python and PyTorch program page here: https://www.edx.org/learn/deep-learning/ibm-deep-learning-with-python-and-pytorch
  • If you wish to register, you must click on ‘Enrol now’ and fill in the details on the sign-up portal. 
  • Enter your full name, public username, appropriate password, email address and country of residence. 
  • Once enrolled, you can access the entire course material and begin your journey.

Filling the form

The Deep Learning with Python and PyTorch training by edX does not ask for any application or registration forms. You simply have to create an online account on the website, and you can enrol yourself into any free and open course.

How it helps

Machine learning is a popular field and area of study that generates a plethora of opportunities. Deep Learning with Python and PyTorch certification is an intermediate course that delves deep into the field of deep learning and teaches complex concepts. You can enrol in the programme for free and start learning on your own time through the self-paced learning feature. Lastly, earning a certificate will help you get better career opportunities.

Instructors

Mr Joseph Santarcangelo

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

FAQs

Is this course suitable for beginners?

No, as this is not an introductory course and will require knowledge of multiple topics.

What is the first course in this series?

The first course is PyTorch Basics for Machine Learning that you must complete being eligible to participate in this programme.

Does the curriculum include any hands-on project?

Yes, after finishing the modules, you must submit a final project.

Do I require previous knowledge of deep learning?

Yes, you require prior knowledge of multiple fields, including machine and deep learning.

Can interested learners from all countries enrol for this course?

Unfortunately, learners from Iran, Cuba, and the Ukraine region of Ukraine cannot enrol due to licensing issues.

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