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learning to apply transfer learning using TensorFlow.js in TypeScript

What you will learn

Basics of transfer learning

Applying transfer learning using TypeScript

Basics of Angular apps using transfer learning

Basics of image classification using machine learning

Description

Welcome to ” Transfer Learning in Angular: learning to apply transfer learning using TensorFlow.js in TypeScript”!

In this comprehensive Udemy course, you’ll embark on a journey to master the art of transfer learning using TensorFlow.js. Transfer learning is a powerful technique that allows you to leverage pre-trained models and apply them to new tasks, saving you time and computational resources.

Throughout this course, you’ll delve into three practical approaches to transfer learning using TensorFlow.js. We’ll start by exploring Teachable Machine, an intuitive and user-friendly platform that enables you to create custom machine learning models without writing a single line of code. You’ll learn how to train your own image classifiers, and then export them as TensorFlow.js models that can be easily integrated into your web applications.

Next, we’ll dive into the K-Nearest Neighbors (KNN) algorithm as a classifier, leveraging the powerful MobileNet as a feature extractor. You’ll discover how to build robust image recognition systems by training the KNN classifier with pre-extracted features from MobileNet, enabling you to classify images with impressive accuracy. We’ll guide you through the implementation process step-by-step, ensuring you gain a solid understanding of the concepts and techniques involved.


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Finally, we’ll equip you with the skills to construct a simple neural network using MobileNet as a feature extractor. You’ll learn how to fine-tune this neural network for specific tasks, such as image classification, by training it on your own custom datasets. By the end of the course, you’ll be capable of developing powerful and versatile models using TensorFlow.js, with MobileNet as your secret weapon.

What sets this course apart is the hands-on approach we adopt throughout. You’ll not only gain theoretical knowledge, but also get plenty of opportunities to put your skills into practice. We’ve designed a series of engaging exercises and coding challenges to ensure you can confidently apply what you’ve learned.

Whether you’re a beginner in machine learning or an experienced developer looking to expand your skillset, this course is tailored to suit your needs. By the end of the course, you’ll have a solid hands-on foundation in transfer learning with TensorFlow.js, enabling you to unlock the full potential of pre-trained models and build sophisticated applications that harness the power of AI.

Enroll now and embark on this exciting journey to become a TensorFlow.js transfer learning expert!

English
language

Content

Getting to know our course

Initial details
Seeing deep learning metaphorically
Details on how transfer learning is on the course

Transfer learning

Initial words
What is transfer learning
Feature extractors for transfer learning
Humans also make transfer learning
Machine learning is a rule finder!

Teachable Machine as a transfer learning platform

Making transfer learning accessable

Using mobilenet as feature extractor, and KNN as classifier

Palavras iniciais
Getting ready to make the feature stack for transfer learning
Creating our feature model
Using our features on the KNN model

Using a feature model based on mobilenet for teaching a neural network

Initial words
Getting ready to transfer learning
Precodes for training from features
Getting our model to extract features from images
Training our model from features from mobilenet
Using our model to separate snakes from bunnies
Advanced: snakes classifications

Closing section

Final words