TensorFlow (Intermediate): 2 Automatic Differentiation

In this course, we continue to work with TensorFlow’s Eager Execution mode and learn about Automatic Differentiation and Gradient Tapes.

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TensorFlow (Intermediate): 2 Automatic Differentiation

Duration (mins)


5.0 / 5


Task List

We will cover the following tasks in 36 minutes:

Introduction and Setup

  • A look at the virtual machine setup.
  • A quick look at what we will create in this course.
  • Introduction to Eager Execution mode.

Derivatives of a Function

  • Importing TensorFlow and enabling Eager Execution.
  • Using Gradients Function to compute derivatives.

Higher Order Gradients

  • Using Gradients Function to calculate higher order derivatives.

Gradient Tapes Part 1

  • What is Gradient Tape?
  • Example of Gradient Tape.

Gradient Tapes Part 2

  • The verbose Gradient Tape context.
  • Gradient with respect to intermediate values computed in a function.

Gradient Tapes for Higher Order Gradients

  • Using the API to find out higher order gradient computation.

Watch Preview

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Amit Yadav

About the Host (Amit Yadav)

I have been writing code since 1993, when I was 11, and my first passion project started with a database management software that I wrote for a local hospital. More recently, I wrote an award winning education Chatbot for a multi-billion-revenue company. I solved a recurrent problem for my client where they wanted to make basic cyber safety and privacy education accessible for their users. This bot enabled my client to reach out to their customers with personalised and real-time education. In the last one year, I’ve continued my interest in this field by constantly learning and growing in Machine Learning, NLP and Deep Learning. I'm very excited to share my variety of experience and learnings with you with the help of Rhyme.com.

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