Deep Learning - Artificial Neural Networks with Tensorflow - Outline

This course focuses on teaching Tensorflow 2 through an API-first, practice-oriented approach. While math is present, it is optional and meant to aid intuition. The course requires basic Python skills and familiarity with numpy and matplotlib. It covers machine learning concepts, including regression, classification, and neural networks, with optional sections on loss functions and gradient descent.

What is the primary focus of this course?

• Data visualization techniques

• API-first and practice-oriented learning

• Historical development of machine learning

• Mathematical theory

How is math treated in this course?

• As a mandatory component

• As a barrier to entry

• As an optional aid for understanding

• As the main focus

What should students do if they encounter something they don't understand in the course?

• Review the prerequisites and fill in knowledge gaps

• Ignore it and move on

• Skip the section entirely

• Ask a classmate for help

What is emphasized as a helpful tool if students get stuck?

• Prerequisites

• Rewatching the video

• Skipping sections

• Asking for help

What is the first section of the course about?

• Advanced neural networks

• Crash course on machine learning

• Data preprocessing

• Visualization techniques

What are the optional sections in the course focused on?

• Data collection methods

• Loss functions and gradient descent

• Visualization techniques

• Historical context of machine learning

What is the analogy used for linear models in the course?

• Programming function

• Biological neuron

• Data point

• Mathematical equation