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WeAreDevelopers LIVE • May 26, 2021

Machine learning in the browser with TensorFlowjs

Håkan Silfvernagel

Want to build privacy-first AI without learning Python? Discover how to train and deploy machine learning models directly in the browser using TensorFlow.js.

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#1 about 2 min

Overview of machine learning in the browser with TensorFlow

Using machine learning models natively in the browser provides powerful client-side capabilities.

#2 about 3 min

Core concepts and use cases of machine learning

Supervised learning and pattern recognition enable sophisticated predictive and clustering applications.

#3 about 2 min

Fundamentals of tensors and the TensorFlow library

Tensors represent data mathematically while TensorFlow handles the underlying distributed computing operations.

#4 about 5 min

Visualizing source data with the TensorFlow visualization component

Fetching JSON data and rendering scatter plots helps identify correlations before training.

#5 about 4 min

Defining a sequential neural network model architecture

Creating a sequential model with dense layers establishes the architecture to process numerical inputs.

#6 about 4 min

Formatting and normalizing data into tensor structures

Converting JavaScript data shapes into tensors and applying min-max scaling ensures optimal training conditions.

#7 about 4 min

Compiling and training the machine learning model

Selecting an optimizer and defining a loss function prepares the model for multi-epoch training.

#8 about 6 min

Testing model predictions and addressing data non-linearity

Generating new tensors tests predictions while adding hidden layers captures non-linear data relationships.

#9 about 2 min

Converting existing Keras models to TensorFlow format

Command line utilities can translate pre-trained Python machine learning models for browser compatibility.

#10 about 2 min

Advantages of running machine learning directly in the browser

Client-side execution removes server round-trips, enhances data privacy, and accesses raw device sensors.

#11 about 5 min

Building an image classifier with a pre-trained model

Loading a converted MobileNet model enables developers to quickly predict classes for user-uploaded images.

#12 about 2 min

Real-world implementations of browser-based machine learning

Major platforms leverage client-side models for visual debugging, intuitive content moderation, and algorithmic music composition.

#13 about 3 min

Learning resources and community platforms for artificial intelligence

Recommended courses, API documentation, and online study groups help advance foundational machine learning skills.

Matching moments

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