Håkan Silfvernagel
Machine learning in the browser with TensorFlowjs
#1about 3 minutes
Understanding the fundamentals of machine learning
Machine learning is defined as pattern recognition in historical data, with supervised learning being a common approach for tasks like prediction and clustering.
#2about 2 minutes
Exploring the TensorFlow library and tensor data structures
TensorFlow is an open-source library that uses tensors, which are multi-dimensional arrays like scalars, vectors, or matrices, to perform computations.
#3about 5 minutes
Loading and visualizing car data with TensorFlow.js
A JSON dataset of car information is loaded and visualized as a scatter plot to identify the negative correlation between horsepower and miles per gallon.
#4about 10 minutes
Building and training a simple sequential model
A sequential model is defined, compiled with an optimizer and loss function, and then trained on normalized and shuffled car data to predict MPG.
#5about 6 minutes
Improving model predictions with additional layers
The initial linear model is improved by adding more dense layers to the neural network, which better captures the non-linear relationship in the data.
#6about 1 minute
Converting and using pre-trained Keras models
Existing models, such as a Keras H5 file, can be converted into the TensorFlow.js layers format using the command-line converter for use in the browser.
#7about 2 minutes
The benefits of running machine learning in the browser
Running machine learning on the client-side eliminates server roundtrips, enhances data privacy, and provides easy access to device sensors like cameras and microphones.
#8about 4 minutes
Building an image classifier with a pre-trained model
A web application is built to classify images by loading a pre-trained MobileNet model that has been converted for TensorFlow.js.
#9about 1 minute
Real-world applications of TensorFlow.js in production
Companies like Uber, Airbnb, and Google's Magenta project use TensorFlow.js for visual debugging, client-side document detection, and music composition.
#10about 2 minutes
Conclusion and further learning resources
Additional resources for learning more about TensorFlow include official documentation, Coursera courses, and the AI 42 online school.
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