TensorFlow: Data and Deployment Specialization

DeepLearning.AI · Beginner ·📐 ML Fundamentals ·4mo ago

Key Takeaways

This specialization covers TensorFlow deployment scenarios, including running models in the browser with JavaScript and on mobile devices, to create value from trained machine learning models.

Full Transcript

Welcome. This specialization will teach you how to take machine learnings that you may have trained and deploy them in TensorFlow. Maybe you've trained models in a Jupyter notebook or in your laptop. But how do you take that model and have it be running 247, have it serve actual user queries and create value? This course will teach you how to do all that. >> Yes. So, we'll be taking a look at like running your models, for example, in the browser with uh JavaScript, even being able to run them on your phone. So we're just going to have a lot of fun looking at models, being able to take what you need to do to your model to be able to convert it to run on all these different form factors. >> For you to be good at machine learning, one of the key skills will be not just the modeling, but also the deployment. One of the most exciting deployment scenarios is in JavaScript so that you can have a neuronet network trained right there in your web browser and camera inference right there in your web browser. >> Yes. So go check it out. We're going to be studying all of that in the next course. So, please go on to the next video.

Original Description

Learn more: https://www.deeplearning.ai/courses/tensorflow-data-and-deployment-specialization/ Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more. What you will learn - Run models in your browser using TensorFlow.js - Prepare and deploy models on mobile devices using TensorFlow Lite - Access, organize, and process training data more easily using TensorFlow Data Services - Explore four advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, and TensorBoard Enroll now: https://www.deeplearning.ai/courses/tensorflow-data-and-deployment-specialization/
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 23 RMSProp (C2W2L07)
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 29 Neural Network Overview (C1W3L01)
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 30 Training Softmax Classifier (C2W3L09)
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 33 Neural Network Representations (C1W3L02)
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 34 TensorFlow (C2W3L11)
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 35 Activation Functions (C1W3L06)
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 37 Getting Matrix Dimensions Right (C1W4L03)
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 47 Normalizing Inputs (C2W1L09)
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 48 Derivatives Of Activation Functions (C1W3L08)
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 49 Parameters vs Hyperparameters (C1W4L07)
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 54 Basic Recipe for Machine Learning (C2W1L03)
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This specialization teaches how to deploy machine learning models trained in TensorFlow to various platforms, including browsers and mobile devices, to create value from these models. It covers the conversion of models to run on different form factors and the deployment of neuronet networks in web browsers for camera inference. By the end of this course, learners will be able to deploy their models and run them on different devices.

Key Takeaways
  1. Take a trained model and convert it for deployment
  2. Deploy the model in a browser using JavaScript
  3. Run the model on a mobile device
  4. Use camera inference in a web browser
  5. Create value from deployed models
💡 Deployment of machine learning models is a crucial skill for creating value from these models, and TensorFlow provides the tools to deploy models on various platforms.

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