filmov
tv
Pruning Deep Learning Models for Success in Production
Показать описание
Research shows that 58% of data scientists are not optimizing their deep learning models for production, despite the significant advantages techniques like pruning and quantization can offer. Why? BECAUSE IT'S HARD!
Mark Kurtz, Machine Learning Lead at Neural Magic, demonstrates how to prune models for performance. He covers an overview of pruning, including its benefits and downsides. He shares easy ways to prune models and showcases tools that make pruning easy and successful. Lastly, Mark shows how to get performance out of a pruned model in production.
Mark Kurtz, Machine Learning Lead at Neural Magic, demonstrates how to prune models for performance. He covers an overview of pruning, including its benefits and downsides. He shares easy ways to prune models and showcases tools that make pruning easy and successful. Lastly, Mark shows how to get performance out of a pruned model in production.
0:24:35
Pruning Deep Learning Models for Success in Production
0:19:46
Quantization vs Pruning vs Distillation: Optimizing NNs for Inference
0:02:01
Pruning a neural Network for faster training times
0:30:08
Reduce Cost and Increase Performance by Pruning Deep Learning Models
0:07:10
PyTorch Pruning | How it's Made by Michela Paganini
0:22:34
Deep Pruning - Methods for Deep Neural Network Optimization
1:09:26
EfficientML.ai Lecture 3 - Pruning and Sparsity (Part I) (MIT 6.5940, Fall 2023)
1:07:07
Lecture 03 - Pruning and Sparsity (Part I) | MIT 6.S965
1:45:19
PhD Thesis Overview (Part 1): Reward is not enough; Towards Goal-Directed, Memory-based Learning
0:03:28
Pruning Makes Faster and Smaller Neural Networks | Two Minute Papers #229
0:14:34
Design Compact Deep Learning Models: Small is the New Big • Davis Sawyer • GOTO 2020
0:15:34
Quantization in deep learning | Deep Learning Tutorial 49 (Tensorflow, Keras & Python)
0:05:00
328 - Holistic Filter Pruning for Efficient Deep Neural Networks
0:02:09
How network pruning can skew deep learning models
1:09:09
Introduction to Deep Learning for Edge Devices Session 4: Pruning
0:01:01
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration
0:42:06
Rebekka Burkholz - Pruning Deep Neural Networks for Lottery Tickets
0:13:27
tinyML Research Symposium 2022: Toward Compact Deep Neural Networks via Energy-Aware Pruning
0:34:18
Data Pruning for Efficient Machine Learning | Ben Sorscher | Eye on AI #117
0:03:41
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
0:12:00
Structured Pruning Learns Compact and Accurate Models
0:22:55
Pruning and Model Compression
0:07:23
Torque Based Structured Pruning for Deep Neural Network
0:00:59
Unstructured vs Structured Pruning in Neural Networks #shorts
Комментарии