Week 3: Mathematical Foundations of DL and Data Pipelines with TF

The Week# 3 of our course broadly covers:

• Review of Deep Learning (DL) concepts
• Mathematical foundations of DL
• Building data pipelines for TensorFlow 2.0

We review basic concepts of Deep Learning in Lecture# 9. We begin with Neural Network representation of Linear and Logistic regression and from there go on to explain basic concepts of Deep Learning.

After explaining the basic concepts, we present a colab on mathematical foundations of Deep Learning. Tensor is a basic data structure for storing data in memory during Deep Learning operations. We cover basics of Tensor and fundamental mathematical operations on tensor that are commonly used in Deep Learning:

• Lecture 10
• Lecture 11

Later in the week, we will explore how to build data pipelines in TensorFlow 2.0 for variety of input formats like CSV, images and text:

• Lecture 12A, Lecture# 12B, Lecture# 12C
• Lecture 13

Hope you enjoy learning TensorFlow 2.0 with us! Do let us know your feedback and ask questions on the course forum.