Training Your Autonomous Vehicle ML Models with Weights & Biases
Skills:
ML Pipelines80%
Weights & Biases makes it easy for autonomous vehicle practitioners to track and understand changes across all your autonomous vehicle experiments. *Visit https://wandb.me/av and sign up for free.*
*Why Weights & Biases?* From updated hyperparameters to changes in the training data. Now your organization can compare and reproduce models, understand results, and iterate quickly without worrying about losing vital work.
Make Weights & Biases your system of record to centralize visibility across experiments, making your team more efficient at turning around models as quickly and safely as possible.
*Transcript*
The Weights & Biases platform is *the* system of record leveraged by ML practitioners building the autonomous vehicles of tomorrow.
From perception and prediction models to planning and state estimation, AV teams use the Weights & Biases platform to centralize visibility across experiments, making them more efficient at turning around models as quickly and safely as possible.
With just a few lines of code, your team will get rich, shareable dashboards which capture the details of their latest experiments, increasing team collaboration and ensuring every model is reproducible.
We know how important rapid experimentation is for teams working on autonomous vehicles. Without a streamlined way to save and monitor their work, machine learning teams can hit costly and lengthy bottlenecks, delaying projects and time to production.
Weights & Biases makes it easy to track and understand changes across experiments, from updated hyperparameters to changes in the training data.
Now your team can compare and reproduce models, understand results and iterate quickly without worrying about losing vital work.
Weights & Biases integrates into all of the ML tools and libraries that you already use like PyTorch, TensorFlow, PyTorch Lightning and lots more.
Simply add a few lines of code to your training scripts to get started monitoring the health of all of your training
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0. What is machine learning?
Weights & Biases
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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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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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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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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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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