Transfer Learning In Angular

learning to apply transfer learning using TensorFlow.js in TypeScript

What you'll learn

Basics of transfer learning

Applying transfer learning using TypeScript

Basics of Angular apps using transfer learning

Basics of image classification using machine learning

Requirements

Tried to explain all, but basics of TensorFlowjs and Angular may be advantageous

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.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!

Overview

Section 1: Getting to know our course

Lecture 1 Initial details

Lecture 2 Seeing deep learning metaphorically

Lecture 3 Details on how transfer learning is on the course

Section 2: Transfer learning

Lecture 4 Initial words

Lecture 5 What is transfer learning

Lecture 6 Feature extractors for transfer learning

Lecture 7 Humans also make transfer learning

Lecture 8 Machine learning is a rule finder!

Section 3: Teachable Machine as a transfer learning platform

Lecture 9 Making transfer learning accessable

Section 4: Using mobilenet as feature extractor, and KNN as classifier

Lecture 10 Palavras iniciais

Lecture 11 Getting ready to make the feature stack for transfer learning

Lecture 12 Creating our feature model

Lecture 13 Using our features on the KNN model

Section 5: Using a feature model based on mobilenet for teaching a neural network

Lecture 14 Initial words

Lecture 15 Getting ready to transfer learning

Lecture 16 Precodes for training from features

Lecture 17 Getting our model to extract features from images

Lecture 18 Training our model from features from mobilenet

Lecture 19 Using our model to separate snakes from bunnies

Lecture 20 Advanced: snakes classifications

Section 6: Closing section

Lecture 21 Final words

JavaScript programmers interested in machine learning,Angular programmers interested in machine learning,Machine learning practitioners interested in Angular/Typescript