qkeras
d2l-en
| qkeras | d2l-en | |
|---|---|---|
| 3 | 6 | |
| 522 | 21,759 | |
| 1.1% | 1.6% | |
| 6.6 | 8.5 | |
| about 2 months ago | 14 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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qkeras
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How to build FPGA-based ML accelerator?
I would check out hls4ml. It's an open source project made by/for people at CERN to convert neural networks created in Python using QKeras (a quantization extension of Keras) into HLS, with Vivado HLS being the most well supported. There are some caveats though, and a fellow student and I have had trouble getting the generated HLS to match the Keras model and be feasible to synthesize, but it seems to work well for smaller neural networks.
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FPGA Neural Network
For quantization-aware training, there's also a tool we integrate with called qkeras: https://github.com/google/qkeras/tree/master/qkeras
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[D] How to Quantize a CNN; And how to deal with a professor...
Brevitas appears to be what you're looking for. I haven't used that but developed something similar myself for a previous project. You could take a look at https://github.com/google/qkeras too
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
What are some alternatives?
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
hls4ml - Machine learning on FPGAs using HLS
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
aimet - AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
conifer - Collect and revisit web pages.
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
Keras - Deep Learning for humans
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.