Master Transfer Learning with TensorFlow.js in Angular

What you will learn:

Transfer learning fundamentals
TensorFlow.js for transfer learning
Angular integration of machine learning models
Image classification with pre-trained models
Building custom image classifiers
Using MobileNet as a feature extractor
Implementing K-Nearest Neighbors (KNN)
Fine-tuning neural networks
Practical application of transfer learning
TypeScript for machine learning projects

Description

Join our comprehensive course on leveraging the power of transfer learning to build intelligent Angular applications!

Discover how to dramatically accelerate your machine learning projects by harnessing the efficiency of transfer learning with TensorFlow.js and TypeScript. This course dives deep into practical applications, guiding you through three distinct approaches to implement transfer learning in your Angular projects.

First, we'll explore the user-friendly Teachable Machine platform. Learn to train custom image classifiers without coding, then seamlessly integrate these models into your Angular applications. Next, master the art of using MobileNet as a powerful feature extractor, paired with the K-Nearest Neighbors (KNN) algorithm for robust image recognition. You'll build high-accuracy image classification systems with minimal training data. Finally, we'll elevate your skills by constructing and fine-tuning simple neural networks, leveraging MobileNet's pre-trained features to create highly adaptable and efficient models.

This hands-on course features step-by-step instructions, coding exercises, and real-world challenges. Whether you're a beginner or an experienced developer, you'll acquire the practical skills needed to build sophisticated AI applications using TensorFlow.js in Angular. Unlock the full potential of pre-trained models and transform your web development capabilities. Enroll today and become a transfer learning expert!

This course is designed for developers of all levels, from those new to machine learning to those seeking to enhance their existing skills with TensorFlow.js and Angular. By combining theoretical knowledge with practical application, you will leave the course ready to confidently build and implement real-world AI solutions.

Curriculum

Course Introduction

This introductory section lays the groundwork for your transfer learning journey. 'Initial details' provides an overview of the course structure and objectives. 'Seeing deep learning metaphorically' helps conceptualize deep learning principles. Finally, 'Details on how transfer learning is on the course' clarifies the course's focus on practical application of transfer learning techniques.

Understanding Transfer Learning

This section delves into the core concepts of transfer learning. 'Initial words' begins the discussion, followed by 'What is transfer learning,' offering a fundamental explanation. 'Feature extractors for transfer learning' explores critical components in the process. 'Humans also make transfer learning' draws parallels between human and machine learning, and 'Machine learning is a rule finder!' provides an intuitive explanation of the machine learning process.

Teachable Machine for Easy Transfer Learning

This section introduces Teachable Machine, a user-friendly platform that simplifies the process of building custom machine learning models. The lecture 'Making transfer learning accessable' provides a hands-on guide to creating and integrating your own image classifiers without extensive coding.

MobileNet & KNN for Image Recognition

This section combines MobileNet's feature extraction capabilities with the KNN algorithm for efficient and accurate image recognition. 'Palavras iniciais' begins the section, followed by 'Getting ready to make the feature stack for transfer learning.' 'Creating our feature model' guides you through the building process, addressing common problems such as the one discussed in 'Solving a problem: mobilenet does not work.' Finally, 'Using our features on the KNN model' showcases the application of the created features.

Building a Neural Network with MobileNet

This section teaches you to build a neural network using MobileNet's extracted features for advanced image classification. 'Initial words' sets the stage, leading to 'Getting ready to transfer learning.' 'Precodes for training from features' provides necessary code snippets, 'Getting our model to extract features from images' outlines the extraction process. 'Training our model from features from mobilenet' teaches the training procedure, followed by an application example in 'Using our model to separate snakes from bunnies.' The section concludes with 'Advanced: snakes classifications,' exploring a more complex scenario.

Course Conclusion

The final section summarizes the key concepts and skills learned throughout the course. 'Final words' provides concluding remarks and resources for further learning.