Welcome and Learning Objectives

Quick TensorFlow

Image Classification with Transfer Learning using TensorFlow 2.x

Creating and Deploying Machine Learning Models to Mobile Devices

Welcome to Quick™ TensorFlow, a series of uniquely designed lessons that quickly help you to gain mastery of Google’s easy-to-use, powerful machine learning framework: TensorFlow.

Let me tell you a little bit about this course, Image Classification with Transfer Learning.

Modern machine learning practitioners that create image recognition models rarely start with training an entire Convolutional Neural Network (CNN) from scratch. The reasons are simple: it is rare to have a dataset large enough and a computer powerful enough to achieve good results in a meaningful time frame. What we do instead is to find a model that was pre-trained on a very large dataset (e.g. ImageNet, which contains about 1.4 million images with 1000 categories), and then we re-purpose that model for the specific task we are interested in. This provides machine learning practitioners with a well trained, highly accurate artificial neural network that becomes readily available with minimal effort! 

In this course, you will learn how to develop powerful machine learning models like these for image classification and object detection, and deploy those models on mobile devices. We will cover the range of topics from convolutional neural networks to transfer learning and TensorFlow Lite.

As a bonus, you will get free, early access to our new tool, Pallet, that makes deploying your models to a mobile phone take minutes instead of days or weeks! 

So, take a look at the syllabus and the hands-on labs for more details on what you’ll learn and we’ll see you inside!

Syllabus and Learning Objectives

Modern Image Recognition

Using Pre-trained Convolutional Neural Networks

Data Preprocessing

Dataset Downloads and Data Preparation using TensorFlow Datasets

Creating Base Model from Pre-trained Convolutional Neural Networks

Creating the Base Model from the MobileNetV2 Model Developed at Google

Freezing the Convolutional Base

Freezing the Base Model's Weights and Biases

Feature Extraction

Creating and Testing the Feature Extraction Capabilities

Adding New Classification Layers

Wrapping the Convolutional Base in a Keras Sequential Model; Adding a New Classification Head.

Stacking Layers, Compiling, Training and Testing the Model

Loss and Optimization

Training Statistics and Predictions

Training and Validation Metrics and Predictions

Readying TensorFlow Models for Deployment to Mobile

Creating and Saving TensorFlow Lite Machine Learning Models

Deploying Models to your Android Phone

Pallet, our No-Code Model Deployment Tool! Quick and Easy Deployment of Machine Learning Models to Your Mobile Phone

Course Structure and Format:

  • Each lecture has accompanying video slides
  • Many lectures have a quiz at the end of the video which are designed to test your understanding of the materials
  • There are several Colabs strategically interspersed throughout the course and tutorials that give you hands-on practice. Please click on the Open in Colab link to run relevant code in the Colab Notebook for lessons covered
  • Once you have completed the Colab section, return to module and click complete and continue (top right corner) to move to the next lesson

Python experience is the only prerequisite, but familiarity w/machine learning & image classification concepts would be helpful. The course includes multiple supplementary tutorials and Colab notebooks with introductory material. (PalletML is no code!)

This mini-course uses Google Colaboratory (Colab). Colab Notebooks are described by Google as an executable document that lets you write, run, and share code within Google Drive. If you are familiar with the Jupyter project, Colab is like a Jupyter Notebook stored in Google Drive. You also receive a free bundled course with 10+ additional tutorials and Colab notebooks that cover many topics related to image classification and transfer leaning with an excellent overview on using Colab!

Quick TensorFlow Mini-Course: Image Classification with Transfer Learning using TensorFlow

Duration: 5-7 hours Early Bird Price: $19.99 Enroll Now!
Free, 3-Months of Access to our Unique Deployment Platform, Pallet! ($30 Value! Limited Time Offer! )

Key Words: Machine Learning, Transfer Learning, Image Classification, Keras, Jupyter Notebook, Colab Notebook