TensorFlow: Data and Deployment Specialization

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

Skills you will gain

Machine Learning
Tensorflow
Advanced Deployment
Object Detection
JavaScript
Convolutional Neural Network
Tensorflow.js
Tensorflow Lite
Mathematical Optimization
Extraction, Transformation And Loading (ETL)
Data Pipelines

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.

Syllabus

You may think that machine learning models can only be trained with supercomputers and big data. This first course shows you how you can train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

Week 1: Training and Inference Using Javascript

Introduction: A conversation with Andrew Ng
Starting the code
Training
First example in code
Using an imported dataset for a multi-class classifier
Looking at the data
One hot encoding
Designing the neural network
Iris classifier in code

Week 2: Running Inference on Models in the Browser

Introduction: A conversation with Andrew Ng
Creating a convolutional net with JavaScript
Visualization of the training
What is a sprite sheet?
Looking deeper into the code
Handwriting classifier in code

Week 3: Running Models in a Browser

Introduction: A conversation with Andrew Ng
Text-based models
Toxicity classifier in code
MobileNet
Using a pre-trained MobileNet
Converting to JavaScript

Week 4: Rock Paper Scissors

Introduction: A conversation with Andrew Ng
Transfer learning
Building a simple web page
Retraining the MobileNet model
Training function
Code to capturing the data
The dataset class
Training the network
Rock paper scissors in code

Instructor

Laurence Moroney

Instructor

Former AI Lead at Google

TensorFlow: Data and Deployment Specialization

What you will learn

Skills you will gain

Syllabus

Week 1: Training and Inference Using Javascript

Week 2: Running Inference on Models in the Browser

Week 3: Running Models in a Browser

Week 4: Rock Paper Scissors

Week 1: Device-based Models with TensorFlow Lite

Week 2: Running a TensorFlow Model in an Android App

Week 3: Building the TensorFlow Model on iOS

Week 4: TensorFlow Lite on Devices

Week 1: Data Pipelines with TensorFlow Data Services

Week 2: Splits and Slices API for Datasets in TensorFlow

Week 3: Exporting your Data into the Training Pipeline

Week 4: Performance

Week 1: TensorFlow Extended

Week 2: Sharing pre-trained models with TensorFlow Hub

Week 3: Tensorboard: tools for model training

Week 4: Federated learning

Course 1: Browser-based Models with TensorFlow.js

Course 2: Device-based Models with TensorFlow Lite

Course 3: Data Pipelines with TensorFlow Data Services

Course 4: Advanced Deployment Scenarios with TensorFlow

Instructor

Laurence Moroney

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