Deep Neural Networks with PyTorch

BY
IBM via Coursera

Enroll in the Deep Neural Networks with PyTorch course and learn deep neural networks and many more.

Lavel

Intermediate

Mode

Online

Duration

2 Weeks

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based
Learning efforts 10 Hours Per Week

Course overview

The Deep Neural Networks with PyTorch certification course will teach candidates, how to use Pytorch to create deep learning models. It is a part of the IBM AI Engineering Professional Certificate. There are a total of 6 courses in that specialisation. The Deep Neural Networks with PyTorch training course is the fourth one of them. Artificial intelligence or AI is revolutionising whole industries by transforming the way businesses exploit knowledge to make decisions across sectors. Organisations need skilled AI engineers who use cutting-edge approaches such as machine learning algorithms and deep learning neural networks to provide their companies with data-driven actionable intelligence in order to remain competitive. This Technical Certificate of 6 courses is designed to equip candidates with the tools which are needed to excel as an Artificial Intelligence or Machine Learning engineer in their career. 

Using programming languages such as Python, candidates will master the basic principles of machine learning and deep learning, which includes supervised and unsupervised learning. Candidates will also obtain a digital badge from IBM acknowledging their proficiency in AI engineering in addition to receiving a Technical Certificate from Coursera.

The highlights

  • 100 percent online course
  • Flexible deadlines
  • Approximately 31 hours to complete
  • Intermediate level 
  • Shareable e-certificate
  • Subtitles in various languages
  • Offered by IBM

Program offerings

  • Videos
  • Recorded content
  • Practice exercises

Course and certificate fees

The Deep Neural Networks with PyTorch certification fee depends, and varies on a month to month basis.

Deep Neural Networks with PyTorch Fees Payment:

Head

Amount

Fee

Rs. 1914/Month




 

certificate availability

Yes

certificate providing authority

Coursera

Who it is for

The candidates who take up this course will usually become python programmers, and ML Engineer

Eligibility criteria

Certification Qualifying Details

For getting certification for the Deep Neural Networks with PyTorch certification by Coursera candidates should complete the whole 7 weeks training. The candidates enrolled for free auditing will not get a certificate of completion. Those who have subscribed by paying the fee will get the certificate.

What you will learn

Knowledge of deep learning

After the completion of the Deep Neural Networks with PyTorch certification syllabus candidates will learn these in depth:

  • The candidate will know how to use Python libraries for Deep Learning applications
  • The candidate can create Deep Neural Networks using PyTorch and will illustrate and apply their knowledge of related machine learning methods and Deep Neural Networks.

The syllabus

Week 1: Exploring Tensors

Videos
  • Course Introduction
  • Introduction to Modern Neural Network
  • Introduction to Matrices and Vectors
  • Introduction to Tensors and Datasets in PyTorch
  • Understanding 1D Tensors in PyTorch
  • Common Operations in 1D Tensors using PyTorch
  • Introduction to 2D Tensors in PyTorch
  • 2D Tensor Operations in PyTorch
  • Understanding Differentiation in PyTorch
reading
  • Course Overview
assignments
  • Practice Quiz: One-Dimensional Tensors
  • Practice Quiz: Two-Dimensional Tensors
  • Graded Quiz: Tensors
app items
  • Lab: Understanding 1D Tensors in PyTorch
  • Lab: Two-Dimensional Tensors
  • Lab: Differentiation in PyTorch
plugins
  • Reading: Helpful Tips for Course Completion
  • Podcast: Summary and Highlights: Tensors

Week 2: Building Datasets in Pytorch

Videos
  • Creating Simple Datasets in PyTorch
  • Building Image Datasets in PyTorch
assignments
  • Practice Quiz: Datasets
  • Graded Quiz: Datasets

Week 2:Building Datasets in Pytorch

app items
  • Lab: Simple Dataset
  • Lab: Torch Vision Datasets
plugin
  • Podcast: Summary and Highlights: Datasets

Week 3: Applying linear Regression and gradient des

Videos
  • Linear Regression in PyTorch
  • Linear Regression Prediction 
  • Training Linear Regression Models
  • Loss Functions
  • Gradient Descent Basics
  • Cost Functions and Batch Gradient Descent
  • PyTorch Linear Regression Training Slope and Bias
assignments
  • Practice Quiz: Linear Regression Prediction and Training
  • Practice Quiz: Gradient Descent
  • Graded Quiz: Linear Regression and Gradient Descent
app items
  • Lab: Linear Regression 1D: Prediction
  • Lab Linear Regression: Prediction
plugins
  • Reading: Best Practices for Training Linear Regression Models in PyTorch
  • Reading: Types of Gradient Descent
  • Reading: Cost Functions
  • Podcast: Summary and Highlights: Linear Regression and Gradient Descent

Week 4: Training linear Regression Models the pytorch

Videos
  • Stochastic Gradient Descent
  • Mini-Batch Gradient Descent
  • Optimization in PyTorch
  • Training, Validation, and Test Split
  • Training, Validation, and Test Split in PyTorch
assignments
  • Practice Quiz: Gradient Descent Methods and Training Workflows in PyTorch
  • Graded Quiz: Linear Regression PyTorch Way
app items
  • Lab: Stochastic Gradient Descent and Data Loader
  • Mini-Batch Gradient Descent
  • Lab: Optimization in PyTorch
  • Lab: Training, Validation, and Test Split in PyTorch
plugin
  • Summary and Highlights: Linear Regression the PyTorch Way

Week 5: Extending linear Regression to Multiple inpu

Videos
  • Multiple Linear Regression Training
  • Multiple Linear Regression Prediction
  • Linear Regression Multiple Outputs
  • Video: Multiple Output Linear Regression Training
  • Current Trends in PyTorch
assignments
  • Practice Quiz: Multiple Input-Output Linear Regression
  • Graded Quiz: Multiple Input Output Linear Regression
app items
  • Lab: Multiple Linear Regression Training
  • Lab: Multiple Linear Regression Prediction
  • Lab: Linear Regression with Multiple Outputs
  • Lab: Training Linear Regression with Multiple Outputs
plugin
  • Summary and Highlights: Multiple Input-Output Linear Regression

Module 6 - Applying logistic Regression for Classification

videos
  • Introduction to Linear Classifiers
  • Sigmoid Function and Probability Thresholding 
  • Logistic Regression Prediction
  • Bernoulli Distribution and Maximum Likelihood Estimation
  • Video: Cross-Entropy Loss in Logistic Regression
  • Applying Cross-Entropy Loss in PyTorch Logistic Regression
  • Advanced Optimization and Training Techniques
  • Regularization and Generalization
assignments
  • Practice Quiz: Logistic Regression for Classification
  • Practice Quiz: Logistic Regression and Cross-Entropy
  • Graded Quiz: Logistic Regression for Classification
app items
  • Logistic Regression Prediction
  • Lab: Logistic Regression Mean Square Error
  • Lab: Logistic Regression Cross Entropy
plugin
  • Summary and Highlights: Logistic Regression for Classification

Module 7 - Final Project, final Quiz,and course wrap-up

readings
  • Congratulations and Next Steps
  • Team and Acknowledgments
assignment
  • Final Exam
peer review
  • Option 2: Peer Graded - Final Project Submission and Evaluation
app items
  • Practice Project: Neural Network for Breast Cancer Classification
  • Final Project: League of Legends Match Predictor
  • Option 1: AI Graded - Final Project: Submission and Evaluation
plugins
  • Final Project Overview
  • Reading: Final Project Submission Guidelines and Deliverables
  • Podcast: Course Wrap-up 

Admission details

To apply for Deep Neural Networks with PyTorch classes admission, candidates need to follow the below given steps:

Step 1: Visit the official programme URL https://www.coursera.org/learn/deep-neural-networks-with-pytorch

Step 2: There is a tab Enroll for free which should be clicked to proceed ahead

Step 3: Create your login id via LinkedIN or Facebook id 

Step 4: Access the programme for 7 days free trial. 

Step 5: Once 7 days come to an end, the candidate needs to then make a fee payment to continue ahead

Scholarship Details

In case a candidate is not able to make the fee payment, then he/she can do so by seeking financial assistance from Coursera. 

How it helps

The Deep Neural Networks with PyTorch certification benefits by being a course is part of the IBM AI Engineering Professional Certificate. By completing the programme candidates will receive a Digital badge from IBM acknowledging their proficiency in AI engineering. That will be a privilege to candidates. Artificial Intelligence and Machine Learning have a big future in this world. Their influences in industries are big nowadays. So as an Artificial Intelligence engineer or Machine Learning engineer candidates will be receiving an up gradation in their career. 

Candidates will be able to find their dream jobs after completing the specialisation. The completion certificate and the Digital Badge from IBM are very valuable when candidates are attending an interview. Job opportunities for AI and Machine Learning engineers are higher in the present world. So the programme will be very worthy. The benefits are not just in the job sector. By learning the course candidates may be able to apply the learning outcomes to their project works very directly. As mentioned above, the opportunities are wide.

Instructors

Mr Joseph Santarcangelo

Mr Joseph Santarcangelo
Data Scientist
IBM

Ph.D

FAQs

How much time is needed to complete Deep Neural Networks with PyTorch programme?

The candidate is supposed to dedicate 31 hours to complete this programme.

If I cannot make the fee payment, can I seek a scholarship?

Currently, Coursera is providing financial assistance to candidates who are not able to afford the fees for the course. 

I do not want to continue the programme, can I quit in between?

The programme gives the facility to access the programme for free for 7 days. So after that once payment is done, the candidate cannot get a refund for the programme.

Who is offering this Deep Neural Networks with PyTorch online course ?

The Deep Neural Networks with PyTorch programme is offered by IBM.

What are some of the subjects that will be covered under this programme?

The Deep Neural Networks with PyTorch programme will deal with subjects namely, Logistic Regression, Softmax Regression, Linear Regression and many others. 

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