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Yan Yan
and
Gady Agam
An introduction to the field of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, transformers, generative models, neural network compression and transfer learning. This course will benefit students’ careers as a machine learning engineer or data scientist.
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What's inside
Syllabus
Module 1: Neural Networks
Welcome to Deep Learning! In module 1, we will give an introduction to deep learning. Deep learning is a branch of machine learning which is based on artificial neural networks. It is capable of learning complex patterns and relationships within data. Particularly, we will discuss feed-forward deep neural network. We will also discuss backpropagation – the way to optimize deep neural networks.
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Syllabus
Module 1: Neural Networks
Welcome to Deep Learning! In module 1, we will give an introduction to deep learning. Deep learning is a branch of machine learning which is based on artificial neural networks. It is capable of learning complex patterns and relationships within data. Particularly, we will discuss feed-forward deep neural network. We will also discuss backpropagation – the way to optimize deep neural networks.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches deep learning and machine learning methods used for object recognition
Instructed by experts in deep learning and machine learning
Covers foundational material in deep learning, including foundational and advanced topics
Suitable for learners with no prior knowledge in deep learning
Provides practical, applicable knowledge and skills
May require access to additional resources, which could incur costs
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Reviews summary
Deep learning: practical and comprehensive foundation
According to learners, this 'Deep Learning' course provides a highly practical and comprehensive introduction, proving especially beneficial for aspiring machine learning engineers and data scientists. Many students highlight the instructor's exceptional clarity in explaining complex topics such as Transformers and Generative Adversarial Networks (GANs). The course is frequently praised for its up-to-date content and numerous hands-on coding exercises, which significantly enhance understanding and real-world application. While largely providing a strong foundational understanding, some students noted that certain areas, like optimization methods or advanced model implementation, could benefit from greater mathematical rigor or more in-depth practical guidance, occasionally prompting the need for supplementary study.
Course's mathematical depth may not satisfy all learners.
"I found the lectures to be quite superficial in certain areas, particularly when dealing with the mathematical underpinnings. As someone with a strong math background, I was hoping for more rigor."
"The course provides a strong foundation, making complex topics accessible even for someone new to the field, but might lack the deep mathematical explanations some experienced learners seek."
Offers valuable coding exercises for applying theoretical knowledge.
"The hands-on coding exercises were invaluable, especially the ones involving PyTorch."
"An excellent course for anyone looking to understand deep learning from a practical standpoint. The focus on applications and real-world scenarios was a major plus for me."
"I found this course incredibly useful for leveling up my deep learning skills. The content is up-to-date and practical."
Covers a wide range of current and essential deep learning topics.
"The course content is very current and relevant to today's ML landscape."
"The course covers a wide range of cutting-edge topics, from foundational neural networks to modern generative models and even model compression."
"The coverage of transformers and diffusion models is a huge plus. The content is up-to-date and practical."
The instructor excels at simplifying complex deep learning concepts.
"The instructor explains complex topics like Transformers and GANs with incredible clarity..."
"The professor's enthusiasm shines through, and their ability to simplify difficult concepts is remarkable."
"Their explanations of backpropagation and LSTMs were the clearest I've encountered."
Forum discussions could be more active for timely support.
"My only minor complaint is that the forum discussions were not very active, making it harder to get questions answered quickly."
"I felt that the discussion on practical tips (Module 3) could have been expanded. More real-world scenarios and common pitfalls would be beneficial."
Some topics may require external resources for deeper understanding.
"My only minor critique is that some explanations, especially for optimization methods, could have been a bit more in-depth. I had to look up external resources for a complete understanding."
"I found myself needing to supplement with other materials to fully grasp the nuances."
"Good overview, but not enough depth. I needed more guidance on implementing complex models from scratch."
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 Learning with these
activities:
Review 'Deep Learning' by Yoshua Bengio
Show steps
Build a strong foundation by reading the seminal textbook on Deep Learning
View
Deep Learning
on Amazon
Show steps
-
Read and summarize Chapter 1: Introduction
-
Read and complete exercises for Chapter 2: Feedforward Neural Networks
-
Read and summarize Chapter 3: Convolutional Neural Networks
Solve practice problems on backpropagation
Show steps
Strengthen your foundation in how neural networks learn
Browse courses on
Backpropagation
Show steps
-
Solve 10 practice problems on backpropagation
-
Debug your code or approach with a peer or TA
Attend weekly study group for discussion and Q&A
Show steps
Deepen your understanding through peer collaboration and exchange of ideas
Browse courses on
Neural Networks
Show steps
-
Regularly attend study group sessions
-
Actively participate in discussions and ask questions
-
Prepare questions or topics to present to the group
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Recurrent Neural Networks' by Alex Graves
Show steps
Expand your knowledge of RNNs and their applications
View
Supervised Sequence Labelling with Recurrent...
on Amazon
Show steps
-
Read and summarize Chapter 1: Introduction to RNNs
-
Read and complete exercises for Chapter 2: LSTM Networks
-
Read and summarize Chapter 3: Applications of RNNs
Build a neural network model for image classification
Show steps
Gain practical experience in applying neural networks to real-world problems
Browse courses on
Neural Networks
Show steps
-
Collect or find a dataset of images
-
Build a neural network model using a framework like PyTorch or TensorFlow
-
Train and evaluate your model
-
Present your results to peers or instructors for feedback
Follow a tutorial on Generative Adversarial Networks
Show steps
Gain exposure to advanced and emerging techniques in Deep Learning
Browse courses on
Generative Adversarial Networks
Show steps
-
Find a comprehensive tutorial on GANs
-
Follow the tutorial step-by-step and implement your own GAN model
-
Share your results with the community or apply them to a project
Contribute to an open-source deep learning library
Show steps
Gain practical experience and network with the deep learning community
Browse courses on
Deep Learning
Show steps
-
Identify a suitable open-source project
-
Fork the project and make a contribution
-
Submit a pull request and engage with the project maintainers
Start a personal project on neural network compression
Show steps
Enhance your understanding of advanced topics and develop your problem-solving abilities
Browse courses on
Deep Learning
Show steps
-
Define the scope and goals of your project
-
Research existing techniques and algorithms
-
Implement and test your own compression methods
-
Evaluate and compare your results against baselines
Review 'Deep Learning' by Yoshua Bengio
Show steps
Build a strong foundation by reading the seminal textbook on Deep Learning
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read and summarize Chapter 1: Introduction
- Read and complete exercises for Chapter 2: Feedforward Neural Networks
- Read and summarize Chapter 3: Convolutional Neural Networks
Solve practice problems on backpropagation
Show steps
Strengthen your foundation in how neural networks learn
Browse courses on
Backpropagation
Show steps
Show steps
Show steps
- Solve 10 practice problems on backpropagation
- Debug your code or approach with a peer or TA
Attend weekly study group for discussion and Q&A
Show steps
Deepen your understanding through peer collaboration and exchange of ideas
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Regularly attend study group sessions
- Actively participate in discussions and ask questions
- Prepare questions or topics to present to the group
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Recurrent Neural Networks' by Alex Graves
Show steps
Expand your knowledge of RNNs and their applications
View
Supervised Sequence Labelling with Recurrent...
on Amazon
Show steps
Show steps
Show steps
- Read and summarize Chapter 1: Introduction to RNNs
- Read and complete exercises for Chapter 2: LSTM Networks
- Read and summarize Chapter 3: Applications of RNNs
Build a neural network model for image classification
Show steps
Gain practical experience in applying neural networks to real-world problems
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Collect or find a dataset of images
- Build a neural network model using a framework like PyTorch or TensorFlow
- Train and evaluate your model
- Present your results to peers or instructors for feedback
Follow a tutorial on Generative Adversarial Networks
Show steps
Gain exposure to advanced and emerging techniques in Deep Learning
Browse courses on
Generative Adversarial Networks
Show steps
Show steps
Show steps
- Find a comprehensive tutorial on GANs
- Follow the tutorial step-by-step and implement your own GAN model
- Share your results with the community or apply them to a project
Contribute to an open-source deep learning library
Show steps
Gain practical experience and network with the deep learning community
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a suitable open-source project
- Fork the project and make a contribution
- Submit a pull request and engage with the project maintainers
Start a personal project on neural network compression
Show steps
Enhance your understanding of advanced topics and develop your problem-solving abilities
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Define the scope and goals of your project
- Research existing techniques and algorithms
- Implement and test your own compression methods
- Evaluate and compare your results against baselines
Career center
Learners who complete Deep Learning will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/gr7x3m/deep
Reading list
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OpenCourser.com/course/gr7x3m/deep
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