Introduction to Pre-Trained Models and TensorFlow.js
Learn about pre-trained models in machine learning and how to use them in TensorFlow.js. Discover different categories of models like vision, body, text, and sound.
00:00:00 Learn about pre-trained models in machine learning and how they can be used without the need for training your own models. Explore an example of a pre-trained model known as Bert q a that performs advanced text search in a web browser and find answers to questions within any piece of text.
🤔 Pre-trained models have already been trained by someone else, saving time and resources.
👩💻 Pre-trained models are available in easy-to-use JavaScript classes for common use cases.
🔍 Example of a pre-trained model called Bert q&a that can perform advanced text search in a web browser.
00:02:22 Learn about pre-trained models and how to use them in TensorFlow.js. Discover different categories of models like vision, body, text, and sound.
📌 Pre-trained models can be used in web apps without a background in machine learning.
🔍 Models exist across various categories such as vision, body, text, and sound.
🐱 Object recognition allows the detection of common objects in images using the Coco SSD model.
00:04:46 An introduction to pre-trained models, specifically the Coco SSD model, which can identify objects in images and provide real-time classification. The model can also be used for sound recognition, text toxicity detection, and face tracking.
🔍 Pre-trained models provide more powerful object recognition by not only identifying where objects are located, but also how many exist.
🖼️ Coco SSD model allows real-time object recognition in web browsers, enabling practical applications like detecting objects and sending alerts.
🎥 Using the webcam, Coco SSD can classify objects in real-time and protect privacy as all the processing is done locally in the web browser.
🔊 Sound recognition models can be used to recognize short sounds and even be retrained for custom sound recognition.
💬 Text toxicity models can automatically identify potentially offensive text, allowing for a more pleasant user experience by hiding such content.
😀 Face mesh model provides high-resolution face tracking and is widely used in various applications.
00:07:09 This video explores pre-trained models, showcasing the combination of face mesh with WebGL shaders for augmented reality makeup Tryon. It also highlights the use of machine learning and 3D graphics in web browsers.
📸 Face mesh combined with webGL shaders for real-time augmented reality makeup try-on.
🤖 Machine learning and 3D graphics libraries like 3.js enable quick development of powerful applications.
⚙️ Ability to switch hardware for execution and the release of two new pose estimation models with improved accuracy and performance.
00:09:33 An overview of pre-trained models and their applications: pose estimation for hand tracking, body segmentation for human body detection, text toxicity filtering for smoother conversations, remote physiotherapy using pose estimation, and automatic size selection on fashion websites.
🔑 Pre-trained models offer various functionalities, such as pose estimation, hand pose tracking, and body segmentation.
🌐 These models have applications in human-computer interaction, web conferencing, remote physiotherapy, and enhancing fashion websites.
💡 By combining pre-trained models with custom code, one can create innovative solutions and enhance user experiences.
00:11:58 Learn how pre-trained models can be used to create advanced visual effects in the browser without requiring machine learning expertise.
⭐ Pre-trained models can be used to replace backgrounds, add special effects, and create digital teleportation in real-time in web browsers.
🌐 These models can be accessed and used by anyone without needing a machine learning background or sending images to a server.
💡 By combining pre-trained models with web technologies, such as WebGL shaders, WebRTC, A-frame, and free.js, new creative experiences can be created.
00:14:22 Learn how to use pre-trained models to enhance your web engineering skills. Try out different models, understand their inputs and outputs, and troubleshoot limitations. Share your findings with classmates.
⭐️ Try out pre-trained models by selecting three from the available options and exploring their documentation and live demos.
💡 Familiarize yourself with the inputs that each model requires, such as image, text, or sound, and the outputs it provides.
🚀 Consider how you can integrate the models into real web applications to solve problems in your own or others' lives.
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