As a computer science student, most of my AI coursework was strictly Python-based. We used PyTorch or TensorFlow in Jupyter notebooks, running on Google Colab GPUs. It was powerful, but it felt disconnected from the web apps I loved building.
For my capstone project, I wanted to bridge that gap. I asked myself: Can I run a neural network directly in the browser, without a backend server?
The Discovery: TensorFlow.js
I stumbled upon TensorFlow.js, a library that lets you define, train, and run models in JavaScript. The coolest part? It uses WebGL to tap into the user's GPU. This means the heavy math of machine learning happens on the client's graphics card, not their CPU.
The Challenge
My first attempt was a simple object detection app using a pre-trained model (COCO-SSD).
- Loading the Model: The model files were huge (MBs). Loading them on a slow network took forever.
- The Lag: Initial inference took 2-3 seconds. The UI froze.
How I Fixed It
To optimize performance, I learned about:
- Quantization: Using smaller, slightly less accurate models (Int8 instead of Float32) to drastically reduce download size.
- Warmups: Running a "dummy" prediction through the network when the page loads to compile the WebGL shaders before the user actually interacts with it.
The Result
I built a webcam filter that detects faces in real-time at 30FPS. It was messy, but it worked. It taught me that the future of AI isn't just massive server farms—it's also edge devices and browsers.
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