Deep Learning - Convolutional Neural Networks with TensorFlow - Outline

This course focuses on teaching Tensorflow 2 through an API-first, practice-oriented approach. While math is not the primary focus, it is included to aid intuition. The course requires basic knowledge of Python and Tensorflow 2, but prerequisites are not barriers. The course covers convolutional neural networks, deep learning tricks, and transfer learning, with an emphasis on practical application.

What is the primary focus of this course?

• Mathematical theory

• API-first and practice-oriented learning

• Hardware optimization for TensorFlow

• Historical development of machine learning

Which of the following is NOT a prerequisite for this course?

• Understanding of feed-forward ANN in TensorFlow 2

• Basic Python programming skills

• Ability to train and predict on tabular data sets

• Advanced calculus knowledge

What is the purpose of including math in this course?

• To ensure students have a strong math background

• To make the course more challenging

• To focus solely on mathematical theory

• To provide optional insight into how things work

How should students view the prerequisites of the course?

• As strict barriers to entry

• As helpful tools for when they get stuck

• As optional guidelines

• As unnecessary requirements

What is the first major topic covered in the course content?

• Convolution in CNNs

• Transfer learning

• Recurrent neural networks

• Data augmentation

Which technique allows leveraging pre-trained neural networks on different data sets?

• Gradient descent

• Transfer learning

• Data augmentation

• Batch normalization

What is a common misconception about NLP in deep learning?

• It is only for text data

• It is not applicable to CNNs

• It requires large data sets

• It can only be done with RNNs