MMdnn
redisai-examples
| MMdnn | redisai-examples | |
|---|---|---|
| 3 | 1 | |
| 5,780 | 55 | |
| 0.1% | - | |
| 0.0 | 0.0 | |
| 7 months ago | about 1 month ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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MMdnn
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[R] Importing TensorFlow neural networks stored as *.h5
The only way I found online is to convert the *.h5 files to MXNet files using Microsoft model management of deep neural networks (link)
- [D] Tools for converting TF code to Pytorch
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Apple’s New M1 Chip is a Machine Learning Beast
Yes. But that's missing the point. Almost everything in the space uses a framework other than Core ML. Therefore most people need good development support for PyTorch/Tensorflow etc., not Core ML. The fact that Apple has a tool to import/convert models is nice, but not relevant. Also, there is onnx as an exchange format between the frameworks, and tools like MMdnn and others.
redisai-examples
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Top 10 Sample Redis Datasets For You
Redis AI Showcase
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
models - Models and examples built with TensorFlow
keras-onnx - Convert tf.keras/Keras models to ONNX
models - A collection of pre-trained, state-of-the-art models in the ONNX format
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
Practical_RL - A course in reinforcement learning in the wild
torchinfo - View model summaries in PyTorch!
EdgeRealtimeVideoAnalytics - An example of using Redis Streams, RedisGears, RedisAI and RedisTimeSeries for Realtime Video Analytics (i.e. counting people)
keras-ncp - PyTorch and TensorFlow implementation of NCP, LTC, and CfC wired neural models
AnimalRecognitionDemo - An example of using Redis Streams, RedisGears and RedisAI for Realtime Video Analytics (i.e. filtering cats)
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.