Key Advantages of TensorFlow.js
- Edge Device Compatibility
- Versatile Deployment
- Enhanced Privacy
Types of Machine Learning
- Supervised Learning: Involves training with labeled data (e.g., image categorization, natural language processing, image segmentation).
- Unsupervised Learning: Extracts patterns from unlabeled data.
- Semi-supervised Learning: Combines both labeled and unlabeled data.
- Reinforcement Learning: Trains models through rewards and penalties.
- Generative Adversarial Networks (GANs): Used in semi-supervised learning.
Recommendations Based on Data Availability
- Supervised/Semi-supervised Learning: Collect a labeled dataset if possible.
- Unsupervised/Reinforcement Learning: Suitable when labeled data is unavailable.
- Game Applications: Reinforcement learning is often the best approach.
Installation and Setup
- Install via NPM (Node Package Manager)
- Install via script tag
Pre-trained Models and Datasets
- Inception v3 & MobileNet: Google AI Team's model architectures, each with unique advantages (MobileNet for low-latency, low-power applications).
- Datasets: Use Oxford-IIIT Pet Dataset for image classification or detection.
- Object Detection Models: Employ SSD (Single Shot Detector) for faster performance compared to R-CNNs.
Practical Tools and Model Conversion
- Model Zoos: Repositories of pre-trained models.
- Model Conversion: Use tfjs-converter to convert models into formats like Open Neural Network Exchange (ONNX) or OpenVINO.
- Teachable Machine: A user-friendly tool by Google for training models directly in the browser using images or audio.
Common Machine Learning Challenges
- Small Amounts of Data: Always aim for more data when possible.
- Poor Data Quality: Ensure data is clean and formatted correctly.
- Data Bias: Prevent algorithmic bias through diverse data.
- Overfitting: Avoid models that only perform well on training data.
- Underfitting: Ensure models are well-trained for generalization.
Google’s Teachable Machine
- a simple tool powered by TensorFlow.js
- image and audio upload supported
- supports webcam capturing for training and creating TensorFlow.js models
- train models directly in the browser
Due to transfer learning, your model will not require much data. Your model will be a transfer learning version of MobileNet.
Additional Tools
- Danfo.js: A JavaScript alternative to Pandas for handling CSV files and converting them to tensors.
- Danfo.js Dnotebook: Equivalent to Jupyter notebooks in JavaScript.