The AI breakthrough

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0. What is machine learning?

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1. Build Your First Machine Learning Model

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Intro to ML: Course Overview

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2. Multi-Layer Perceptrons

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3. Convolutional Neural Networks

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Weights & Biases at OpenAI

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Why Experiment Tracking is Crucial to OpenAI

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4. Autoencoders

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5. Sentiment Analysis

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6. Recurrent Neural Networks [RNNs]

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7. Text Generation using LSTMs and GRUs

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8. Text Classification Using Convolutional Neural Networks

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9. Hybrid LSTMs [Long Short-Term Memory]

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Toyota Research Institute on Experiment Tracking with Weights & Biases

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Weights and Biases - Developer Tools for Deep Learning

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Introducing Weights & Biases

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10. Seq2Seq Models

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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets

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12. One-shot learning for teaching neural networks to classify objects never seen before

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13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow

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14. Data Augmentation | Keras

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15. Batch Size and Learning Rate in CNNs

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Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)

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Grading Rubric for AI Applications with Sergey Karayev (2019)

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16. Video Frame Prediction using CNNs and LSTMs (2019)

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Image to LaTeX - Applied Deep Learning Fellowship (2019)

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17. Build and Deploy an Emotion Classifier (2019)

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Applied Deep Learning - Data Management with Josh Tobin (2019)

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Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)

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Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)

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Troubleshooting and Iterating ML Models with Lee Redden (2019)

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Designing a Machine Learning Project with Neal Khosla (2019)

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Lukas Beiwald on ML Tools and Experiment Management (2019)

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Building Machine Learning Teams with Josh Tobin (2019)

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Pieter Abeel on Potential Deep Learning Research Directions (2019)

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Testing and Deployment of Deep Learning Models with Josh Tobin (2019)

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Five Lessons for Team-Oriented Research with Peter Welder (2019)

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Applied Deep Learning - Rosanne Liu on AI Research (2019)

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Making the Mid-career Leap from Urban Design to Deep Learning/Data Science

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Organizing ML projects — W&B walkthrough (2020)

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Brandon Rohrer — Machine Learning in Production for Robots

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Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars

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My experiments with Reinforcement Learning with Jariullah Safi

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Applications of Machine Learning to COVID-19 Research with Isaac Godfried

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Testing Machine Learning Models with Eric Schles

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How Linear Algebra is not like Algebra with Charles Frye

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Predicting Protein Structures using Deep Learning with Jonathan King

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Rachael Tatman — Conversational AI and Linguistics

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Reformer by Han Lee

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Sequence Models with Pujaa Rajan

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GitHub Actions & Machine Learning Workflows with Hamel Husain

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Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye

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Jack Clark — Building Trustworthy AI Systems

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Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye

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Track your machine learning experiments locally, with W&B Local - Chris Van Pelt

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Antipatterns in open source research code with Jariullah Safi

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Attention for time series forecasting & COVID predictions - Isaac Godfried

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Made with ML - Goku Mohandas

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Angela & Danielle — Designing ML Models for Millions of Consumer Robots

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Deep Learning Salon by Weights & Biases

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