Hands On Training

Getting Started with Tensorflow 2.0

Pluralsight

Course Features

Duration

189 minutes

Delivery Method

Online

Available on

Downloadable Courses

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Beginner

Teaching Type

Self Paced

Video Content

189 minutes

Course Description

TensorFlow 2.0 is a popular framework for training and building neural network models. It combines the functionality and performance of other frameworks with the ease of TensorFlow. With TensorFlow 2.0, designing and training neural networks becomes easy, and debugging and prototyping models are simplified.

In this course, you will learn the basics of a neural network, including its active learning unit and the neuron. You will also explore the differences between static and dynamic computation graphs, understanding the benefits and drawbacks of each type.

In TensorFlow 2.0's eager execution mode, you will delve into TensorFlow execution and discover how to harness the performance of static graphs using the tf.function decorator.

Additionally, this course covers Keras, which has long supported sequential models that consist of layers stacked on top of each other. You will explore the Functional API and Model Subclassing in Keras, learning how to use these APIs to build both regression and classification models.

Whether you are just getting started with artificial intelligence or already have experience in data science and TensorFlow, this course will provide you with the knowledge and skills needed to effectively use TensorFlow 2.0 for neural network training and model building.

Course Overview

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

What You Will Learn

Learning unit, the neuron

Next, you will compare and contrast static and dynamic computation graphs and understand the advantages and disadvantages of working with each kind of graph

You will get hands-on exploring execution in TensorFlow 2

0 in eager execution mode and harness the performance efficiencies of static graphs by using the tf

Function decorator to decorate ordinary Python functions

You will then learn how a neural network is trained using gradient descent optimization and how the GradientTape() library in TensorFlow calculates gradients automatically during the training phase of your neural network model

Finally, you will learn how different APIs in Keras lend themselves to different use-cases

Sequential models consisting of layers stacked one on top of the other are simple and have long been supported by Keras

You will also explore the Functional API and model subclassing in Keras and then use these APIs to build regression as well as classification modelsWhen you’re finished with this course, you will have the skills and knowledge to harness the computational

0 framework and choose between the different model-building strategies available in Keras

Course Content

Expand all sections

Module 1: Exploring the TensorFlow 2.0 Framework

Module 2: Understanding Dynamic and Static Computation Graphs

Module 3: Computing Gradients for Model Training

Module 4: Using the Sequential API in Keras

Course Instructors

Janani Ravi

Instructor

Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework...