TensorFlow Beginner: Add-Ons for TensorFlow

Name: TensorFlow Beginner: Add-Ons for TensorFlow
Author: Josh Turner

Discover the add-ons available for TensorFlow that can enhance your machine learning projects

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Course: On-Demand

Hello, my name is Josh Turner, and these lessons you'll learn about TensorFlow Hub. You'll learn about TensorFlow deployment and packages, and you'll learn about TensorFlow APIs and packages. Let's get started. TensorFlow Hub is a library that provides pre-trained models and modules for TensorFlow. It allows developers to quickly and easily use state-of-the-art models for a wide range of use cases. This includes image recognition, NLP, or natural language processing, and many more, for example, speech to text. The first thing you need to do is open a Python IDE of your choice. Once you've done that, open the terminal and go to the PowerShell or Linux terminal. You can see down here in the command line, in yellow, I've typed pip install TensorFlow-hub. If you're using a Jupyter Notebook, instead, put an exclamation point in front of pip and run it in a cell of the Jupyter Notebook. OK, once you've run that line of code, you can import it into the Python IDE with the following line of code. Just type import TensorFlow underscore hub as hub. Once you've done that, you can run this line of code to make sure everything works on your local environment. OK, great, so far, so good. Now let's browse a few of the TensorFlow hub models. Let's look at a vision model, an NLP model, and an audio model. The first one that we'll look at is a vision model. I'll follow the same procedure in each step. I'll type for you the model URL, which will be https colon double forward slash tf hub dot dev slash Google. After that, the rest of the text will depend on the model. For vision models, we'll use the ImageNet mobile dev V2 model. This is a model that has been trained on thousands of images and makes for a very good pre-trained classifier. We can load the model with vision module equal to hub dot module. OK, there's an example of a vision model. Next, let's look at an example of an NLP model. These models might take a long time to load, so you can look at them at your own discretion. This follows the exact same path as before, except, again, we change the model name. We'll use the universal sentence encoder for this model. This is a model that's been trained on many, many sentences to try to understand context and semantics from text. We load it the same way as we loaded the vision model. The final model that we can browse in TensorFlow Hub is an audio model. For us, the audio model will be the non-semantic speech benchmark model. Again, we'll type the model URL. We'll begin the same as before. And we'll load the model in the exact same way as before. This model has been trained on many different types of speech and makes for a gray audio model for things like voice to text or text to voice. OK, you can run these lines of code if you want. It'll take a while for the model to load, but you can do it at your discretion. OK, let's continue. Let's look at an example of a pre-trained model. All right, the first thing that we should do is run the NLP module code. Once you run this code, let's make a sentence. Heavy boxers perform quick waltzes and jigs. Once you've typed this line of code, go ahead and run it. OK, great. We've saved this variable into Python's memory. After you created your line of code, typed the following piece of code. This will be very useful for many different NLP tasks. So let's go ahead and run this line of code using our pre-trained model to change the sentence into a vector. We start with a sentence. Let's see what vector we get. All right, great. You see we turned our sentence into a vector of numbers. Thanks for watching. Stay tuned next time when we'll discuss TensorFlow deployment.

Beginner Provider : Josh Turner 3 Lessons · 13m in Arabic, German, English, Spanish, French, Portuguese, Chinese

Course Description

TensorFlow is a powerful tool for machine learning, but there are also a number of add-ons available that can enhance its capabilities. In these lessons, you will explore the different add-ons available and how to use them effectively in your machine learning projects.

You’ll start with an exploration of different True/False statements related to add-ons for TensorFlow, such as the usefulness of TensorFlow Hub for finding pretrained models and the server capabilities of TensorFlow. You will learn about tools such as TensorFlow Lite, which is designed for devices with limited CPU power; and the TensorFlow Probability API, which includes features such as Monte Carlo algorithms and Markov chains.

You will also learn about popular modules for machine learning, such as scikit-learn, and how to use Python libraries such as requests and JSON to communicate with TensorFlow servers. Additionally, you will explore the different aspects of the deployment process, such as saving a model and setting up a server.

Using tools such as TensorFlow Hub, you will learn to find and use pretrained models, and how to use TensorFlow Lite to deploy your models on devices with limited CPU power. You will also explore the different features available in the TensorFlow Probability API and how they can be used to enhance your machine learning models.

Overall, you will gain a foundational understanding of the different add-ons available for TensorFlow and the skills necessary to use them effectively in your machine learning projects.

What You'll Learn

Explore the different add-ons available for TensorFlow and how to use them effectively in machine learning projects
Use TensorFlow Hub to find and use pretrained models
Deploy models on devices with limited CPU power using TensorFlow Lite
Use Python libraries such as requests and JSON to communicate with TensorFlow servers
Examine the features of the TensorFlow Probability API, including Monte Carlo algorithms and Markov chains
Work through the deployment process, such as saving a model and setting up a server

Key Takeaways

TensorFlow has a number of add-ons available that can enhance its capabilities for machine learning projects.
TensorFlow Hub is useful for finding pretrained models, while TensorFlow Lite is designed for devices with limited CPU power.
The TensorFlow Probability API includes features such as Monte Carlo algorithms and Markov chains.
Python libraries such as requests and JSON can be used to communicate with TensorFlow servers.
The deployment process covers aspects such as saving a model and setting up a server.

Frequently Asked Questions

Who is this course for?

It is a beginner-level course for those wanting a foundational understanding of the different add-ons available for TensorFlow and the skills to use them effectively in machine learning projects.

What add-ons does the course cover?

It covers tools such as TensorFlow Hub for finding pretrained models, TensorFlow Lite for devices with limited CPU power, the TensorFlow Probability API, and modules such as scikit-learn.

What will I learn about deployment?

You will explore aspects of the deployment process such as saving a model and setting up a server, and using Python libraries such as requests and JSON to communicate with TensorFlow servers.

What topics are included in the lessons?

The lessons cover TensorFlow Hub, TensorFlow Deployment, and Additional APIs and Packages.

What skills are associated with this course?

The course relates to skills including Computational Tools, ML.NET, PyTorch, TensorFlow, and Training Datasets.