Panelists Suz Hinton and Nick Nisi discuss TensorFlow.js and Machine Learning in JavaScript with special guest Paige Bailey, TensorFlow mom and developer Advocate for Google AI.

Leave us a comment

Changelog++ members support our work, get closer to the metal, and make the ads disappear. Join today!

Sponsors:

• Rollbar – We move fast and fix things because of Rollbar. Resolve errors in minutes. Deploy with confidence. Learn more at rollbar.com/changelog.
• Raygun – Unblock your biggest app performance bottlenecks with Raygun APM. Smarter application performance monitoring (APM) that lets you understand and take action on software issues affecting your customers.
• OneMonth.com – One of the best places to learn how to code…in just one month. If you’re interested in taking your career to the next level head to OneMonth.com/jsparty and get 10% off any coding course.
• Linode – Our cloud server of choice. Deploy a fast, efficient, native SSD cloud server for only $5/month. Get 4 months free using the code changelog2018. Start your server - head to linode.com/changelog

Featuring:

• Paige Bailey – Twitter, GitHub, Website
• Suz Hinton – Twitter, GitHub, Website
• Nick Nisi – Twitter, GitHub, Website

Show Notes:

• TensorFlow.js
• Google AI
  ml5.js - Friendly Machine Learning for the Web
• Machine Learning Glossary
• TensorFlow tutorials
• Tero Parviainen on CodePen
• tfjs-layers - High-level machine learning model API
• tfjs-models - Pre-trained TensorFlow.js models
• tfma-slicing-metrics-browser.gif 📷
• TensorFlow Model Analysis (TFMA) - a library for evaluating TensorFlow models
• What-If Tool - Building effective machine learning systems means asking a lot of questions. It’s not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better.
• EthicalMachineLearning.ipynb
• TensorBoard: Visualizing Learning
• TensorBoard: Graph Visualization
• People + AI Research (PAIR) - Human-centered research and design to make AI partnerships productive, enjoyable, and fair.
• Distill - Clear explanations of machine learning
• Book: Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech
• Book: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
• A new course to teach people about fairness in machine learning
• List of cognitive biases
• CleverHans - a Python library to benchmark machine learning systems’ vulnerability to adversarial examples
• CleverHans paper
• Breaking linear classifiers on ImageNet
• CV Dazzle - explores how fashion can be used as camouflage from face-detection technology, the first step in automated face recognition

Something missing or broken? PRs welcome!