inflearn logo

Deep Learning

This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.

1 learners are taking this course

Level Beginner

Course period Unlimited

visualization
visualization
graphics
graphics
MIT
MIT
visualization
visualization
graphics
graphics
MIT
MIT
Thumbnail

What you will gain after the course

  • Design and implement various neural network architectures

  • Analyze and optimize deep learning models for specific tasks

  • Apply deep learning techniques to solve problems in computer vision and NLP

Recommended for
these people

Who is this course right for?

  • Data scientists struggling to implement deep learning models

  • Software engineers wanting to transition into AI roles

  • Researchers looking to apply deep learning in their projects

Need to know before starting?

  • Familiarity with basic statistics and probability concepts

  • Understanding of machine learning fundamentals

  • Experience with programming in Python or similar languages

Hello
This is Open Academy

1,614

Learners

8

Reviews

5.0

Rating

105

Courses

"So that language does not become a barrier to learning."

We deliver open lectures from the world's leading institutions.
Through translation and subtitling, we help all learners follow the lectures without the burden of the original language.

More

Curriculum

All

27 lectures ∙ (29hr 31min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

Not enough reviews.
Please write a valuable review that helps everyone!

Open Academy's other courses

Check out other courses by the instructor!

Similar courses

Explore other courses in the same field!

Free