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Joseph Santarcangelo
The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
Register for this course and see more details by visiting:
OpenCourser.com/course/uhtqsf/deep
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What's inside
Syllabus
Tensor and Datasets
Linear Regression
Linear Regression PyTorch Way
Read more
Syllabus
Tensor and Datasets
Linear Regression
Linear Regression PyTorch Way
Read more
Multiple Input Output Linear Regression
Logistic Regression for Classification
Softmax Rergresstion
Shallow Neural Networks
Deep Networks
Convolutional Neural Network
Peer Review
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a foundation in PyTorch, an essential framework in deep learning
Covers fundamental concepts such as linear regression and logistics regression
Progresses to advanced topics like feedforward deep neural networks and CNNs
Designed for learners with a background in deep learning concepts
Save this course
Save Deep Neural Networks with PyTorch to your list so you can find it easily later:
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Reviews summary
Pytorchdnn
learners say this course effectively teaches the basics of Deep Neural Networks using PyTorch. Many reviewers appreciate the thorough, hands-on labs that accompany the engaging assignments. Other positive attributes of the course mentioned by reviewers include:
- well-paced content
- clear explanations
- lots of examples
- applicable to real-world situations
The course content is well-structured and easy to follow.
"I really enjoyed the course for its diversity and practicality"
"This was a great course and it covers a diverse set of subjects."
"The course is very complete and the instructor demonstrates a lot of knowledge on the subject."
"it is freeaaakin hard if you take the whole IBM AI ENGINEERING Professional Cert in the duration of a trial period."
The hands-on labs are very helpful in reinforcing concepts.
"The labs helped reinforce the content of the videos."
"it systematically gives how to use pytorch doing neural network."
"This course is full of great information. A bit long. Since this is part of a series, some of the information is a bit repetitive."
"I really enjoy this course. it really helps me to boost my knowledge of PyTorch and deep neural network."
This course does a great job introducing the fundamentals of DNNs and using PyTorch.
"The course content was very well presented and was relatively easy to understand even when the pytorch framework is a bit complex."
"This course is full of great information."
"it is systematically gives how to use pytorch doing neural network."
"This is an excellent course that cover the theory and implmentaton of deep neural networks using Pytorch!"
The explanations in the videos are unclear at times.
"The presentation is not good enough."
"The slides and exercises are full of spelling errors."
"The artificial voice with the lectures to be distracting."
"This course is disorganized and unfocused. For example, much of the section on Bernoulli distribution is misleading or completely incorrect."
There are too many errors in the quizzes and slides.
"There are so many spelling errors in the quizes; the lab loads slowly."
"The quizzes are a complete joke."
"There are too many typos in the notebooks and quizzes, even wrong indices of materials, including videos and notebooks."
"The material and instruction is really nice. But so many typo especially in the quiz."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Deep Neural Networks with PyTorch with these
activities:
Review Linear Algebra and Calculus
Show steps
This course requires working knowledge of matrix algebra, linear transformations, and multivariable calculus.
Browse courses on
Linear Algebra
Show steps
-
Go through your old notes or textbook on the topics if you have them
-
Look at the relevant sections of some online references on these topics
-
Do some problems related to these topics
Explore PyTorch tutorials
Show steps
Deepen your understanding of PyTorch concepts by following guided tutorials from experts.
Browse courses on
PyTorch
Show steps
-
Find reputable online tutorials on PyTorch
-
Follow the tutorials step-by-step
-
Experiment with different code examples
Practice coding with PyTorch
Show steps
Reinforce your understanding of PyTorch by completing practice drills and exercises.
Browse courses on
PyTorch
Show steps
-
Set up your PyTorch environment
-
Follow online tutorials to solve coding problems
-
Solve problems from online coding platforms
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Tensor Manipulation Practice
Show steps
Helps you to familiarize with the operations on tensors in PyTorch
Browse courses on
Tensors
Show steps
-
Complete the exercises in this page: https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html
PyTorch Lightning Tutorial
Show steps
Provides a structured learning experience with PyTorch Lightning
Browse courses on
PyTorch
Show steps
-
Set up your environment and install PyTorch Lightning.
-
Go through the following tutorial: https://pytorch-lightning.readthedocs.io/en/latest/lightning-quickstart.html
-
Read the documentation and try working with the library.
Build a deep learning model
Show steps
Apply your PyTorch skills by building a deep learning model for a real-world problem.
Browse courses on
Deep Learning
Show steps
-
Identify a suitable problem
-
Gather and pre-process data
-
Design and train your model
-
Evaluate and refine your model
Build and Train a Simple CNN
Show steps
Hands-on experience building a CNN model and observe its performance
Browse courses on
Convolutional Neural Networks
Show steps
-
Choose a dataset
-
Define and train a simple CNN model
-
Evaluate and improve the model
Deep Learning Project Idea Presentation
Show steps
Gives you the opportunity to propose and present your own deep learning project idea
Browse courses on
Deep Learning
Show steps
-
Brainstorm and finalize a project idea
-
Prepare a presentation to introduce your project and its potential impact.
-
Present your project idea
Volunteer as a Teaching Assistant
Show steps
Provides an opportunity to review the material and strengthen your understanding
Show steps
-
Reach out to the instructors and ask about opportunities to assist with teaching activities
-
Contribute to discussions, answer questions from classmates, and assist with assignments
Contribute to a PyTorch Project
Show steps
Gives you exposure to real-world PyTorch projects and contributes to the open-source community
Browse courses on
Open Source
Show steps
-
Choose a PyTorch project to contribute to
-
Set up your development environment
-
Identify an issue to work on or propose a feature
-
Submit a pull request
Review Linear Algebra and Calculus
Show steps
This course requires working knowledge of matrix algebra, linear transformations, and multivariable calculus.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go through your old notes or textbook on the topics if you have them
- Look at the relevant sections of some online references on these topics
- Do some problems related to these topics
Explore PyTorch tutorials
Show steps
Deepen your understanding of PyTorch concepts by following guided tutorials from experts.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Find reputable online tutorials on PyTorch
- Follow the tutorials step-by-step
- Experiment with different code examples
Practice coding with PyTorch
Show steps
Reinforce your understanding of PyTorch by completing practice drills and exercises.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Set up your PyTorch environment
- Follow online tutorials to solve coding problems
- Solve problems from online coding platforms
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Tensor Manipulation Practice
Show steps
Helps you to familiarize with the operations on tensors in PyTorch
Browse courses on
Tensors
Show steps
Show steps
Show steps
- Complete the exercises in this page: https://pytorch.org/tutorials/beginner/basics/tensorqs_tutorial.html
PyTorch Lightning Tutorial
Show steps
Provides a structured learning experience with PyTorch Lightning
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Set up your environment and install PyTorch Lightning.
- Go through the following tutorial: https://pytorch-lightning.readthedocs.io/en/latest/lightning-quickstart.html
- Read the documentation and try working with the library.
Build a deep learning model
Show steps
Apply your PyTorch skills by building a deep learning model for a real-world problem.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a suitable problem
- Gather and pre-process data
- Design and train your model
- Evaluate and refine your model
Build and Train a Simple CNN
Show steps
Hands-on experience building a CNN model and observe its performance
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Choose a dataset
- Define and train a simple CNN model
- Evaluate and improve the model
Deep Learning Project Idea Presentation
Show steps
Gives you the opportunity to propose and present your own deep learning project idea
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Brainstorm and finalize a project idea
- Prepare a presentation to introduce your project and its potential impact.
- Present your project idea
Volunteer as a Teaching Assistant
Show steps
Provides an opportunity to review the material and strengthen your understanding
Show steps
Show steps
Show steps
- Reach out to the instructors and ask about opportunities to assist with teaching activities
- Contribute to discussions, answer questions from classmates, and assist with assignments
Contribute to a PyTorch Project
Show steps
Gives you exposure to real-world PyTorch projects and contributes to the open-source community
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Choose a PyTorch project to contribute to
- Set up your development environment
- Identify an issue to work on or propose a feature
- Submit a pull request
Career center
Learners who complete Deep Neural Networks with PyTorch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are professionals with backgrounds in computer science, software development, or a related technology field who create machine-learning models and systems.They write code, analyze data, and implement machine-learning algorithms to solve problems like image and speech recognition, fraud detection, and predictive analytics. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Data Scientist
Data Scientist
Data Scientists are specialists in extracting knowledge from data using scientific methods, processes, algorithms and systems. Typically holding an advanced degree in a field such as mathematics, statistics, computer science, or information science, Data Scientists specialize in analyzing data to uncover patterns and trends, then visualizing and communicating their findings to decision-makers. This course may be useful in building a foundation in deep learning, a subfield of machine learning which has shown great promise in a wide range of applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They analyze user needs, gather and analyze requirements, and design and implement software solutions. Software Engineers in various industries use deep learning to solve problems and improve outcomes. This course may be useful in building a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and make predictions about financial markets. This course may be useful in building a foundation in deep learning, a subfield of machine learning which has shown great promise in a wide range of applications including financial analysis.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns.They use their findings to make recommendations and inform decision-makers. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs, gather and analyze data, and develop recommendations. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use their findings to make recommendations and inform decision-makers. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with stakeholders to understand their needs, gather and analyze data, and develop product specifications. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to analyze and solve problems in a variety of industries. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze and make recommendations on investment opportunities. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
User Experience Researcher
User Experience Researcher
User Experience Researchers use data to understand and improve the user experience. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Market Research Analyst
Market Research Analyst
Market Research Analysts use data to analyze and understand consumer behavior. This course may be useful as it will help build a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course may be useful in building a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
Database Administrator
Database Administrator
Database Administrators maintain and administer databases. This course may be useful in building a foundation in deep learning, a subfield of machine learning, which is increasingly used in various industries.
For more career information including salaries, visit:
OpenCourser.com/course/uhtqsf/deep
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Deep Neural Networks with PyTorch.
Provides a comprehensive introduction to deep learning using PyTorch. It covers the basics of deep learning, including tensors, neural networks, and training algorithms. It also provides a number of practical examples of how to use PyTorch to solve real-world problems.
Save
Provides a comprehensive overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of pattern recognition and machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical introduction to deep learning using Fastai and PyTorch. It covers the basics of deep learning, including tensors, neural networks, and training algorithms. It also provides a number of practical examples of how to use Fastai and PyTorch to solve real-world problems.
Provides a comprehensive overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of machine learning from a probabilistic perspective, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of statistical learning theory, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of statistical learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Provides a practical introduction to machine learning for hackers, including topics such as data preprocessing, model training, and model evaluation.
Provides a comprehensive overview of the mathematics used in machine learning, including topics such as linear algebra, calculus, and probability.
Provides a comprehensive introduction to deep learning using Python. It covers the basics of deep learning, including tensors, neural networks, and training algorithms. It also provides a number of practical examples of how to use Python to solve real-world problems.
Save
Provides a comprehensive introduction to deep learning. It covers the basics of deep learning, including tensors, neural networks, and training algorithms. It also provides a number of practical examples of how to use deep learning to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uhtqsf/deep
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Level
Intermediate
Via
Coursera
Institution
IBM
Instructor
Joseph Santarcangelo
Language
English
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Good to know
Provides a foundation in PyTorch, an essential framework in deep learning
Covers fundamental concepts such as linear regression and logistics regression
Progresses to advanced topics like feedforward deep neural networks and CNNs
Designed for learners with a background in deep learning concepts
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