Evaluation Comparisons in W&B Weave
The *“Compare Evaluations”* button makes it easy to compare different evaluations in a comprehensive report. In a single view, you can compare the performance, latency, and token usage of different evaluations.
Watch one of the lead Weights & Biases SWE's use the visual evaluations in W&B Weave.
Ready to get started? If you need more help getting started, check out some top resources:
*-Weave Documentation:* https://wandb.github.io/weave/?utm_source=send&utm_medium=email&utm_campaign=weave_newsletter
*-Weave Quickstart Guide:* https://wandb.github.io/weave/quickstart?utm_source=send&utm_med…
Watch on YouTube ↗
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Chapters (8)
Introduction to Evaluation Comparison tool
0:23
Building comprehensive reports
1:00
Summary metrics overview
1:30
Model scorecard details
2:37
Example filter and output comparison
4:25
Exploring challenging examples
6:29
Dynamic mobile view support
7:18
Single vs. multiple evaluations
Playlist
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![6. Recurrent Neural Networks [RNNs]](https://i.ytimg.com/vi/8lbGjKhrJOo/mqdefault.jpg)
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![9. Hybrid LSTMs [Long Short-Term Memory]](https://i.ytimg.com/vi/NysY9FN9Uac/mqdefault.jpg)
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0. What is machine learning?
Weights & Biases
1. Build Your First Machine Learning Model
Weights & Biases
Intro to ML: Course Overview
Weights & Biases
2. Multi-Layer Perceptrons
Weights & Biases
3. Convolutional Neural Networks
Weights & Biases
Weights & Biases at OpenAI
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Why Experiment Tracking is Crucial to OpenAI
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4. Autoencoders
Weights & Biases
5. Sentiment Analysis
Weights & Biases
6. Recurrent Neural Networks [RNNs]
Weights & Biases
7. Text Generation using LSTMs and GRUs
Weights & Biases
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
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
Weights & Biases
10. Seq2Seq Models
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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
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
Weights & Biases
14. Data Augmentation | Keras
Weights & Biases
15. Batch Size and Learning Rate in CNNs
Weights & Biases
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
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
Weights & Biases
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
VDLS Lavanya Product Walkthrough
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Testing Machine Learning Models with Eric Schles
Weights & Biases
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
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
Weights & Biases
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
Weights & Biases
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
Weights & Biases
Made with ML - Goku Mohandas
Weights & Biases
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Weights & Biases
DeepCamp AI