TensorFlow.js and Machine Learning in JavaScript
Show notes
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. Join the discussion 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 – Website, GitHub, X Suz Hinton – GitHub, Mastodon, X Nick Nisi – Website, GitHub, Mastodon, X 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!
Guests
Analysis: done · source seed