Getting Started with Tensorflow 2.0

This course focuses on introducing the TensorFlow 2.0 framework - exploring the features and functionality that it offers for building and training neural networks. This course discusses how TensorFlow 2.0 differs from TensorFlow 1.x and how the use of the Keras high-level API and eager execution makes TensorFlow 2.0 a very easy to work with even for complex models.
Course info
Rating
(13)
Level
Beginner
Updated
Jul 23, 2020
Duration
3h 9m
Table of contents
Course Overview
Exploring the TensorFlow 2.0 Framework
Understanding Dynamic and Static Computation Graphs
Computing Gradients for Model Training
Using the Sequential API in Keras
Using the Functional API and Model Subclassing in Keras
Description
Course info
Rating
(13)
Level
Beginner
Updated
Jul 23, 2020
Duration
3h 9m
Description

TensorFlow has long been a powerful and widely used framework for building and training neural network models. In recent years though other frameworks such as PyTorch have gained popularity specifically due to their intuitive programming model which uses dynamic execution graphs. Now TensorFlow 2.0 offers all the ease of use of other frameworks along with TensorFlow's performance and functionality. TensorFlow's use of the Keras high-level API makes designing and training neural networks very straightforward while eager execution makes prototyping and debugging models simple.

First, you will explore the basic features in TensorFlow 2.0 and how its programming model differs from TensorFlow 1.x versions. You will understand the basic working of a neural network and its active 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 models

When you’re finished with this course, you will have the skills and knowledge to harness the computational power of the TensorFlow 2.0 framework and choose between the different model-building strategies available in Keras.

About the author
About the author

A problem solver at heart, 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.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Getting Started with TensorFlow 2.0. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real‑time collaborative editing in Google Docs, and I hold four patents for its underlining technologies. I currently work on my own startup, Loonycorn, a studio for high‑quality video content. TensorFlow has long been a powerful and widely‑used framework for building and training neural network models. TensorFlow 2.0's use of the Keras high‑level API makes designing and training neural networks very straightforward, while its eager execution mode makes prototyping and debugging models very simple. In this course, first you will explore the basic features in TensorFlow 2.0, and how its programming model differs from TensorFlow 1x versions. You will understand the basic working of a neural network and its active 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 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. Finally, you'll work with different APIs in Keras, and see how they lend themselves to different use cases. We'll build sequential models, which consists of layers stacked one on top of the other. You will also explore the functional API and model subclassing in Keras, and then use these APIs to build regression, as well as classification models. When you're finished with this course, you will have the skills and knowledge to harness the computational power of the TensorFlow 2.0 framework, and choose between the different model‑building strategies available in Keras.