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Deep Neural Networks with PyTorch

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.

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What's inside

Syllabus

Tensor and Datasets
Linear Regression
Linear Regression PyTorch Way
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Traffic lights

Read about what's good
what should give you pause
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

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Reviews summary

Introduction to deep learning with pytorch

According to learners, this course provides a good introduction to PyTorch and covers fundamental neural network architectures, including feedforward networks and CNNs. Many found the hands-on labs and coding exercises particularly valuable for practical understanding and application. While it serves as a solid starting point for beginners in deep learning with PyTorch, some reviewers note that it lacks depth for more experienced practitioners and may require prior knowledge of Python and basic machine learning concepts. The peer review assignment was mentioned by some as an area where feedback quality and consistency could be improved.
Provides a solid starting point with PyTorch.
"As a complete beginner to PyTorch, I found this course accessible and well-paced."
"Great introduction to the basics of deep learning using the PyTorch framework."
"Assumes basic Python knowledge but introduces PyTorch tensors and autograd clearly."
Hands-on coding solidifies learning.
"The labs were the best part, really helped me understand the concepts by doing."
"Lots of practical coding examples provided that I could easily follow and modify."
"I appreciated the emphasis on applying PyTorch directly through exercises and notebooks."
Grading consistency varies greatly.
"The peer assessment process for the final project felt very subjective and inconsistent."
"It was frustrating not getting detailed or useful feedback from peers on my work."
"The quality of peer grading varied significantly among reviewers I received."
Assumes some prior Python/ML knowledge.
"Struggled a bit in the coding exercises without a stronger Python background."
"Some parts went quickly assuming prior knowledge of basic machine learning models."
"Prospective students should be aware they need solid Python skills beforehand to keep up."
Too basic for intermediate or advanced users.
"Felt it only scratched the surface on topics like CNNs and transfer learning."
"Needed more advanced concepts and discussion on optimization techniques beyond the basics."
"If you already have a background in deep learning, this course might be too simple for you."

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
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
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
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
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
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
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
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
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
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
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

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 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.
See salaries and explore the career path for Machine Learning Engineer
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Quantitative 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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Product Manager
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.
See salaries and explore the career path for Financial 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.
See salaries and explore the career path for Operations Research 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.
See salaries and explore the career path for Risk 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.
See salaries and explore the career path for Market Research Analyst
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.
See salaries and explore the career path for User Experience Researcher
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.
See salaries and explore the career path for Data Engineer
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.
See salaries and explore the career path for Database Administrator

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.
Cover image
Deep Learning with PyTorch
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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.
Deep Learning with PyTorch: Build, train, and tune...
Paperback
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Deep Learning with PyTorch: Build, train, and tune...
Kindle Edition
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Machine Learning
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Provides a comprehensive overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Machine Learning (McGraw-Hill International...
Paperback
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Pattern Recognition and Machine Learning
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Provides a comprehensive overview of pattern recognition and machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning (text...
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Deep Learning for Coders with fastai and PyTorch
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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.
Deep Learning for Coders with Fastai and PyTorch:...
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Machine Learning, revised and updated edition
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Provides a comprehensive overview of machine learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Machine Learning, revised and updated edition (The...
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Machine Learning, revised and updated edition (The...
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Probabilistic Machine Learning
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Provides a comprehensive overview of machine learning from a probabilistic perspective, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
Probabilistic Machine Learning: Advanced Topics...
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The Elements of Statistical Learning
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Provides a comprehensive overview of statistical learning theory, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
The Elements of Statistical Learning: Data Mining,...
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The Elements of Statistical Learning
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Provides a comprehensive overview of statistical learning, including topics such as supervised learning, unsupervised learning, and reinforcement learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
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The Elements of Statistical Learning: Data Mining,...
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The Elements of Statistical Learning: Data Mining,...
Kindle Edition
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Machine Learning for Hackers
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Provides a practical introduction to machine learning for hackers, including topics such as data preprocessing, model training, and model evaluation.
Machine Learning for Hackers: Case Studies and...
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Machine Learning for Hackers: Case Studies and...
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Mathematics for Machine Learning
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Provides a comprehensive overview of the mathematics used in machine learning, including topics such as linear algebra, calculus, and probability.
Mathematics for Machine Learning
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Deep Learning with Python, Second Edition
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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.
Deep Learning with Python, Second Edition
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Deep Learning with Python, Second Edition
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Deep Learning
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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.
Deep Learning
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Deep Learning (Adaptive Computation and Machine...
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Deep Learning (Adaptive Computation and Machine...
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Course image
Level
Intermediate
Via
Coursera
Institution
IBM
Instructor
Joseph Santarcangelo
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
Deep Neural Networks with PyTorch belongs to the following collections:
  1. IBM AI Engineering
  2. IBM AI Engineering
Traffic lights
Read about what's good
what should give you pause
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
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