Mathematics behind backpropagation : theory and Python Code.

Format:

Video

Author/Creator:

Szepesi, Patrik, author.

Contributor:

Packt Publishing, publisher.

Language:

English

Subjects (All):

Back propagation (Artificial intelligence).

Python (Computer program language).

Physical Description:

1 online resource (1 video file (4 hr., 37 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Birmingham, United Kingdom] : Packt Publishing, 2026.

Summary:

In this 4-hour course, you will gain a deep understanding of the mathematics behind backpropagation and its implementation in Python. The course will cover everything from the basics of derivatives and gradients to building and training your own neural network from scratch. What I will be able to do after this course Master the mathematics of backpropagation for neural networks Understand derivatives, partial derivatives, and gradients Implement backpropagation from scratch using Python code Dive deep into gradient descent and learning rates Explore the significance of computational graphs in AI Course Instructor(s) Patrik Szepesi is a senior Machine Learning Engineer with experience in autonomous vehicles, banking, and healthcare. He has contributed to groundbreaking projects at Morgan Stanley and John Deere, and published research in top journals. Patrik currently works in healthcare AI development and holds advanced AWS certifications. Who is it for? This course is perfect for data scientists, aspiring machine learning engineers, and software developers who want to master the mathematical foundation of backpropagation. It's ideal for those transitioning into machine learning with basic Python knowledge.

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781807608439

OCLC:

1589022835