Tutorial: Math for Deep Learning
The math background knowledge of students attending our lectures on deep learning, computer vision and self-driving cars is diverse. In response, we created a series of tutorials (3.5 hours in total) on relevant concepts (linear algebra, differential calculus, probability theory, information theory), primarily based on the Goodfellow and Bishop books. These micro lectures are offered to students who like to recap relevant or unknown course prerequisites through self-study. They also introduce the relevant notation that we use in our lectures.
Further Readings
- Goodfellow, Bengio and Courville: Deep Learning
- Bishop: Pattern Recognition and Machine Learning
- Deisenroth, Faisal and Ong: Mathematics for Machine Learning
Schedule
|
| |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|